Overview

Dataset statistics

Number of variables75
Number of observations3979
Missing cells118443
Missing cells (%)39.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory600.0 B

Variable types

CAT44
NUM29
BOOL2

Warnings

O_product_name_fr has a high cardinality: 2505 distinct values High cardinality
O_product_name has a high cardinality: 2598 distinct values High cardinality
O_brands has a high cardinality: 330 distinct values High cardinality
O_brands_tags_str has a high cardinality: 215 distinct values High cardinality
O_brands_tags has a high cardinality: 215 distinct values High cardinality
O_serving_size has a high cardinality: 164 distinct values High cardinality
O_countries_hierarchy has a high cardinality: 107 distinct values High cardinality
O_cities_tags has a high cardinality: 148 distinct values High cardinality
O_manufacturing_places has a high cardinality: 163 distinct values High cardinality
O_purchase_places has a high cardinality: 272 distinct values High cardinality
O_countries_tags has a high cardinality: 107 distinct values High cardinality
O_categories has a high cardinality: 755 distinct values High cardinality
O_category_properties has a high cardinality: 124 distinct values High cardinality
O_compared_to_category has a high cardinality: 240 distinct values High cardinality
O_ingredients_text has a high cardinality: 2466 distinct values High cardinality
O_ingredients has a high cardinality: 2376 distinct values High cardinality
O_salt_content has a high cardinality: 1842 distinct values High cardinality
O_oil_content has a high cardinality: 1168 distinct values High cardinality
O_nutriments has a high cardinality: 3349 distinct values High cardinality
O_BRANDS_UPPER has a high cardinality: 226 distinct values High cardinality
S_brands has a high cardinality: 258 distinct values High cardinality
S_BRANDS_UPPER has a high cardinality: 217 distinct values High cardinality
N_SAME_PRODUCT has a high cardinality: 782 distinct values High cardinality
N_RECETTES has a high cardinality: 477 distinct values High cardinality
N_ITEM has a high cardinality: 1432 distinct values High cardinality
N_NIELSEN_DESCRIPTION has a high cardinality: 1212 distinct values High cardinality
I_ITEM has a high cardinality: 1272 distinct values High cardinality
O_energy_kcal_100g is highly correlated with O_energy_100gHigh correlation
O_energy_100g is highly correlated with O_energy_kcal_100g and 1 other fieldsHigh correlation
O_fat_100g is highly correlated with O_energy_100gHigh correlation
O_salt_100g is highly correlated with O_sodium_100gHigh correlation
O_sodium_100g is highly correlated with O_salt_100gHigh correlation
df_index is highly correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
Unnamed: 0.1 is highly correlated with df_index and 1 other fieldsHigh correlation
N_EAN13 is highly correlated with O_EAN13High correlation
O_EAN13 is highly correlated with N_EAN13High correlation
N_Ventes_Valeur_2018 is highly correlated with N_Ventes_Valeur_2019High correlation
N_Ventes_Valeur_2019 is highly correlated with N_Ventes_Valeur_2018High correlation
N_INITIAL_INDEX is highly correlated with Unnamed: 0.1 and 1 other fieldsHigh correlation
O_pnns_groups_2 is highly correlated with O_pnns_groups_1High correlation
O_pnns_groups_1 is highly correlated with O_pnns_groups_2High correlation
O_date_modified is highly correlated with O_interface_version_modifiedHigh correlation
O_interface_version_modified is highly correlated with O_date_modifiedHigh correlation
S_BRAND_TYPE is highly correlated with S_BRAND and 1 other fieldsHigh correlation
S_BRAND is highly correlated with S_BRAND_TYPE and 1 other fieldsHigh correlation
N_MARQUE is highly correlated with S_BRAND and 1 other fieldsHigh correlation
N_ORGANIC is highly correlated with N_GAMMEHigh correlation
N_GAMME is highly correlated with N_ORGANIC and 1 other fieldsHigh correlation
N_CATEGORY is highly correlated with N_GAMMEHigh correlation
O_EAN13 has 545 (13.7%) missing values Missing
O_product_name_fr has 674 (16.9%) missing values Missing
O_product_name has 560 (14.1%) missing values Missing
O_brands has 545 (13.7%) missing values Missing
O_brands_tags_str has 545 (13.7%) missing values Missing
O_brands_tags has 545 (13.7%) missing values Missing
O_serving_size has 3440 (86.5%) missing values Missing
O_serving_quantity has 3440 (86.5%) missing values Missing
O_countries_hierarchy has 545 (13.7%) missing values Missing
O_cities_tags has 1519 (38.2%) missing values Missing
O_manufacturing_places has 3226 (81.1%) missing values Missing
O_purchase_places has 2809 (70.6%) missing values Missing
O_countries_tags has 545 (13.7%) missing values Missing
O_categories has 984 (24.7%) missing values Missing
O_category_properties has 545 (13.7%) missing values Missing
O_pnns_groups_1 has 546 (13.7%) missing values Missing
O_pnns_groups_2 has 545 (13.7%) missing values Missing
O_compared_to_category has 1009 (25.4%) missing values Missing
O_interface_version_modified has 549 (13.8%) missing values Missing
O_ingredients_n has 1437 (36.1%) missing values Missing
O_ingredients_text has 1437 (36.1%) missing values Missing
O_ingredients has 712 (17.9%) missing values Missing
O_salt_content has 712 (17.9%) missing values Missing
O_max_salt_content has 545 (13.7%) missing values Missing
O_min_salt_content has 545 (13.7%) missing values Missing
O_oil_content has 712 (17.9%) missing values Missing
O_max_oil_content has 545 (13.7%) missing values Missing
O_min_oil_content has 545 (13.7%) missing values Missing
O_oil_type has 3031 (76.2%) missing values Missing
O_nutriments has 545 (13.7%) missing values Missing
O_energy_100g has 670 (16.8%) missing values Missing
O_energy_kcal_100g has 1288 (32.4%) missing values Missing
O_saturated_fat_100g has 699 (17.6%) missing values Missing
O_fat_100g has 688 (17.3%) missing values Missing
O_carbohydrates_100g has 686 (17.2%) missing values Missing
O_sugars_100g has 693 (17.4%) missing values Missing
O_fruits_vegetables_nuts_estimate_from_ingredients_100g has 1438 (36.1%) missing values Missing
O_proteins_100g has 672 (16.9%) missing values Missing
O_sodium_100g has 698 (17.5%) missing values Missing
O_salt_100g has 698 (17.5%) missing values Missing
O_nutrition_score_fr_100g has 1163 (29.2%) missing values Missing
O_fiber_100g has 2422 (60.9%) missing values Missing
O_nova_group_100g has 1659 (41.7%) missing values Missing
O_COUNT has 545 (13.7%) missing values Missing
O_date_modified has 549 (13.8%) missing values Missing
O_BRANDS_UPPER has 545 (13.7%) missing values Missing
S_brands has 2004 (50.4%) missing values Missing
S_BRAND has 2004 (50.4%) missing values Missing
S_BRAND_TYPE has 2004 (50.4%) missing values Missing
S_BRANDS_UPPER has 2004 (50.4%) missing values Missing
Unnamed: 0.1 has 2536 (63.7%) missing values Missing
df_index has 2536 (63.7%) missing values Missing
N_EAN13 has 2536 (63.7%) missing values Missing
N_MARQUE has 2536 (63.7%) missing values Missing
N_SAME_PRODUCT has 2536 (63.7%) missing values Missing
N_GAMME has 2547 (64.0%) missing values Missing
N_RECETTES has 2547 (64.0%) missing values Missing
N_ORGANIC has 2536 (63.7%) missing values Missing
N_FORMAT has 2547 (64.0%) missing values Missing
N_WEIGHT_num has 2536 (63.7%) missing values Missing
N_EMBALLAGE has 2536 (63.7%) missing values Missing
N_COMPTE has 2547 (64.0%) missing values Missing
N_STD/PROMO has 2547 (64.0%) missing values Missing
N_Ventes_Valeur_2019 has 2536 (63.7%) missing values Missing
N_Ventes_Valeur_2018 has 2536 (63.7%) missing values Missing
N_ITEM has 2547 (64.0%) missing values Missing
N_NIELSEN_DESCRIPTION has 2536 (63.7%) missing values Missing
N_INITIAL_INDEX has 2536 (63.7%) missing values Missing
N_CATEGORY has 2536 (63.7%) missing values Missing
N_COUNT has 2536 (63.7%) missing values Missing
I_ITEM has 2707 (68.0%) missing values Missing
I_CATEGORY has 2867 (72.1%) missing values Missing
I_Ventes_Volume_2018 has 2536 (63.7%) missing values Missing
I_Ventes_Volume_2019 has 2536 (63.7%) missing values Missing
O_min_salt_content is highly skewed (γ1 = 20.57410725) Skewed
O_fruits_vegetables_nuts_estimate_from_ingredients_100g is highly skewed (γ1 = 48.9606071) Skewed
N_Ventes_Valeur_2019 is highly skewed (γ1 = 33.90402418) Skewed
N_Ventes_Valeur_2018 is highly skewed (γ1 = 33.4951491) Skewed
O_ingredients_text is uniformly distributed Uniform
Unnamed: 0.1 is uniformly distributed Uniform
N_ITEM is uniformly distributed Uniform
N_NIELSEN_DESCRIPTION is uniformly distributed Uniform
I_ITEM is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
O_max_salt_content has 1807 (45.4%) zeros Zeros
O_min_salt_content has 2962 (74.4%) zeros Zeros
O_max_oil_content has 2488 (62.5%) zeros Zeros
O_min_oil_content has 2801 (70.4%) zeros Zeros
O_saturated_fat_100g has 264 (6.6%) zeros Zeros
O_fat_100g has 103 (2.6%) zeros Zeros
O_carbohydrates_100g has 99 (2.5%) zeros Zeros
O_sugars_100g has 200 (5.0%) zeros Zeros
O_fruits_vegetables_nuts_estimate_from_ingredients_100g has 885 (22.2%) zeros Zeros
O_proteins_100g has 62 (1.6%) zeros Zeros
O_nutrition_score_fr_100g has 87 (2.2%) zeros Zeros
O_fiber_100g has 229 (5.8%) zeros Zeros
N_Ventes_Valeur_2019 has 271 (6.8%) zeros Zeros
N_Ventes_Valeur_2018 has 297 (7.5%) zeros Zeros
I_Ventes_Volume_2018 has 467 (11.7%) zeros Zeros
I_Ventes_Volume_2019 has 447 (11.2%) zeros Zeros

Reproduction

Analysis started2020-10-08 10:51:41.432476
Analysis finished2020-10-08 10:54:03.041862
Duration2 minutes and 21.61 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIQUE

Distinct3979
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1989
Minimum0
Maximum3978
Zeros1
Zeros (%)< 0.1%
Memory size31.1 KiB
2020-10-08T12:54:03.152573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile198.9
Q1994.5
median1989
Q32983.5
95-th percentile3779.1
Maximum3978
Range3978
Interquartile range (IQR)1989

Descriptive statistics

Standard deviation1148.782689
Coefficient of variation (CV)0.5775679684
Kurtosis-1.2
Mean1989
Median Absolute Deviation (MAD)995
Skewness0
Sum7914231
Variance1319701.667
MonotocityStrictly increasing
2020-10-08T12:54:03.333967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
27001< 0.1%
 
6771< 0.1%
 
27241< 0.1%
 
6731< 0.1%
 
27201< 0.1%
 
6691< 0.1%
 
27161< 0.1%
 
6651< 0.1%
 
27121< 0.1%
 
Other values (3969)396999.7%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
39781< 0.1%
 
39771< 0.1%
 
39761< 0.1%
 
39751< 0.1%
 
39741< 0.1%
 

O_EAN13
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3430
Distinct (%)99.9%
Missing545
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean4.538409561e+12
Minimum8712
Maximum8.715700421e+13
Zeros0
Zeros (%)0.0%
Memory size31.1 KiB
2020-10-08T12:54:03.511173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8712
5-th percentile2.227152015e+12
Q13.256220879e+12
median3.560070278e+12
Q35.601831452e+12
95-th percentile8.712100614e+12
Maximum8.715700421e+13
Range8.71570042e+13
Interquartile range (IQR)2.345610573e+12

Descriptive statistics

Standard deviation2.992412962e+12
Coefficient of variation (CV)0.6593527802
Kurtosis285.4550665
Mean4.538409561e+12
Median Absolute Deviation (MAD)3.375968249e+11
Skewness11.07336969
Sum1.558489843e+16
Variance8.954535336e+24
MonotocityNot monotonic
2020-10-08T12:54:03.716602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.038359008e+1220.1%
 
3.038359005e+1220.1%
 
8715721520.1%
 
3.038359005e+1220.1%
 
3.18381101e+121< 0.1%
 
3.222472263e+121< 0.1%
 
3.264963017e+121< 0.1%
 
3.038354191e+121< 0.1%
 
8.715700017e+121< 0.1%
 
3.256220666e+121< 0.1%
 
Other values (3420)342086.0%
 
(Missing)54513.7%
 
ValueCountFrequency (%) 
87121< 0.1%
 
419851< 0.1%
 
3332141< 0.1%
 
4961171< 0.1%
 
13264061< 0.1%
 
ValueCountFrequency (%) 
8.715700421e+131< 0.1%
 
8.000403018e+131< 0.1%
 
9.800001268e+121< 0.1%
 
9.556041613e+121< 0.1%
 
9.556041612e+121< 0.1%
 

O_product_name_fr
Categorical

HIGH CARDINALITY
MISSING

Distinct2505
Distinct (%)75.8%
Missing674
Missing (%)16.9%
Memory size31.1 KiB
Tomato Ketchup
 
34
Ketchup
 
29
Sauce bolognaise
 
24
Double concentré de tomates
 
21
Houmous
 
19
Other values (2500)
3178 
ValueCountFrequency (%) 
Tomato Ketchup340.9%
 
Ketchup290.7%
 
Sauce bolognaise240.6%
 
Double concentré de tomates210.5%
 
Houmous190.5%
 
Bolognaise190.5%
 
Sauce barbecue190.5%
 
Tomato ketchup180.5%
 
Olives vertes dénoyautées150.4%
 
Sauce tomate basilic130.3%
 
Other values (2495)309477.8%
 
(Missing)67416.9%
 
2020-10-08T12:54:03.900018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2238 ?
Unique (%)67.7%
2020-10-08T12:54:05.085814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length255
Median length20
Mean length21.6908771
Min length3

O_product_name
Categorical

HIGH CARDINALITY
MISSING

Distinct2598
Distinct (%)76.0%
Missing560
Missing (%)14.1%
Memory size31.1 KiB
Ketchup
 
33
Tomato Ketchup
 
32
Sauce bolognaise
 
24
Double concentré de tomates
 
20
Tomato ketchup
 
20
Other values (2593)
3290 
ValueCountFrequency (%) 
Ketchup330.8%
 
Tomato Ketchup320.8%
 
Sauce bolognaise240.6%
 
Double concentré de tomates200.5%
 
Tomato ketchup200.5%
 
Sauce barbecue190.5%
 
Houmous190.5%
 
Bolognaise190.5%
 
Pesto alla genovese160.4%
 
Pesto rosso160.4%
 
Other values (2588)320180.4%
 
(Missing)56014.1%
 
2020-10-08T12:54:05.271504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2325 ?
Unique (%)68.0%
2020-10-08T12:54:05.448617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length255
Median length21
Mean length22.20507665
Min length3

O_brands
Categorical

HIGH CARDINALITY
MISSING

Distinct330
Distinct (%)9.6%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
Auchan
289 
U
273 
Carrefour
273 
Heinz
256 
Panzani
 
164
Other values (325)
2179 
ValueCountFrequency (%) 
Auchan2897.3%
 
U2736.9%
 
Carrefour2736.9%
 
Heinz2566.4%
 
Panzani1644.1%
 
Casino1433.6%
 
Sacla1343.4%
 
Cora1052.6%
 
Barilla892.2%
 
Zapetti631.6%
 
Other values (320)164541.3%
 
(Missing)54513.7%
 
2020-10-08T12:54:05.628800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique139 ?
Unique (%)4.0%
2020-10-08T12:54:05.799205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length48
Median length6
Mean length7.127670269
Min length1

O_brands_tags_str
Categorical

HIGH CARDINALITY
MISSING

Distinct215
Distinct (%)6.3%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
['auchan']
293 
['carrefour']
281 
['u']
273 
['heinz']
264 
['panzani']
 
171
Other values (210)
2152 
ValueCountFrequency (%) 
['auchan']2937.4%
 
['carrefour']2817.1%
 
['u']2736.9%
 
['heinz']2646.6%
 
['panzani']1714.3%
 
['sacla']1624.1%
 
['casino']1443.6%
 
['cora']1082.7%
 
['barilla']922.3%
 
['zapetti']661.7%
 
Other values (205)158039.7%
 
(Missing)54513.7%
 
2020-10-08T12:54:05.976034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique65 ?
Unique (%)1.9%
2020-10-08T12:54:06.156205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length55
Median length10
Mean length10.61673787
Min length3

O_brands_tags
Categorical

HIGH CARDINALITY
MISSING

Distinct215
Distinct (%)6.3%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
['auchan']
293 
['carrefour']
281 
['u']
273 
['heinz']
264 
['panzani']
 
171
Other values (210)
2152 
ValueCountFrequency (%) 
['auchan']2937.4%
 
['carrefour']2817.1%
 
['u']2736.9%
 
['heinz']2646.6%
 
['panzani']1714.3%
 
['sacla']1624.1%
 
['casino']1443.6%
 
['cora']1082.7%
 
['barilla']922.3%
 
['zapetti']661.7%
 
Other values (205)158039.7%
 
(Missing)54513.7%
 
2020-10-08T12:54:06.342030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique65 ?
Unique (%)1.9%
2020-10-08T12:54:06.509029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length55
Median length10
Mean length10.61673787
Min length3

O_serving_size
Categorical

HIGH CARDINALITY
MISSING

Distinct164
Distinct (%)30.4%
Missing3440
Missing (%)86.5%
Memory size31.1 KiB
100g
75 
40 g
 
32
15 g
 
30
50 g
 
25
200 g
 
20
Other values (159)
357 
ValueCountFrequency (%) 
100g751.9%
 
40 g320.8%
 
15 g300.8%
 
50 g250.6%
 
200 g200.5%
 
100 g170.4%
 
400 g170.4%
 
20 g160.4%
 
10 g140.4%
 
25 g120.3%
 
Other values (154)2817.1%
 
(Missing)344086.5%
 
2020-10-08T12:54:06.680008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique107 ?
Unique (%)19.9%
2020-10-08T12:54:06.851600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length57
Median length3
Mean length3.444081427
Min length2

O_serving_quantity
Real number (ℝ≥0)

MISSING

Distinct78
Distinct (%)14.5%
Missing3440
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean104.6846939
Minimum0
Maximum440
Zeros5
Zeros (%)0.1%
Memory size31.1 KiB
2020-10-08T12:54:07.052187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q128.125
median78
Q3110
95-th percentile372
Maximum440
Range440
Interquartile range (IQR)81.875

Descriptive statistics

Standard deviation103.74653
Coefficient of variation (CV)0.9910381947
Kurtosis1.596636545
Mean104.6846939
Median Absolute Deviation (MAD)47
Skewness1.516565086
Sum56425.05
Variance10763.34249
MonotocityNot monotonic
2020-10-08T12:54:07.200996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100972.4%
 
15491.2%
 
40370.9%
 
50310.8%
 
200230.6%
 
400230.6%
 
10220.6%
 
20210.5%
 
300180.5%
 
25170.4%
 
Other values (68)2015.1%
 
(Missing)344086.5%
 
ValueCountFrequency (%) 
050.1%
 
3.751< 0.1%
 
4.51< 0.1%
 
560.2%
 
6.81< 0.1%
 
ValueCountFrequency (%) 
44020.1%
 
400230.6%
 
39020.1%
 
3701< 0.1%
 
35040.1%
 

O_countries_hierarchy
Categorical

HIGH CARDINALITY
MISSING

Distinct107
Distinct (%)3.1%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
['en:france']
2898 
['en:belgium', 'en:france']
 
97
['en:france', 'en:spain']
 
64
['en:france', 'en:switzerland']
 
64
['en:france', 'en:germany']
 
34
Other values (102)
 
277
ValueCountFrequency (%) 
['en:france']289872.8%
 
['en:belgium', 'en:france']972.4%
 
['en:france', 'en:spain']641.6%
 
['en:france', 'en:switzerland']641.6%
 
['en:france', 'en:germany']340.9%
 
['en:france', 'en:germany', 'en:switzerland']330.8%
 
['en:italy']270.7%
 
['en:france', 'en:italy']210.5%
 
['en:france', 'en:united-kingdom']170.4%
 
['en:france', 'en:united-states']140.4%
 
Other values (97)1654.1%
 
(Missing)54513.7%
 
2020-10-08T12:54:07.372861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique72 ?
Unique (%)2.1%
2020-10-08T12:54:07.570799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length150
Median length13
Mean length14.37245539
Min length3

O_cities_tags
Categorical

HIGH CARDINALITY
MISSING

Distinct148
Distinct (%)6.0%
Missing1519
Missing (%)38.2%
Memory size31.1 KiB
[]
1916 
['camaret-sur-aigues-vaucluse-france']
 
64
['monteux-vaucluse-france']
 
39
['virazeil-lot-et-garonne-france']
 
28
['castelnaudary-aude-france']
 
15
Other values (143)
398 
ValueCountFrequency (%) 
[]191648.2%
 
['camaret-sur-aigues-vaucluse-france']641.6%
 
['monteux-vaucluse-france']391.0%
 
['virazeil-lot-et-garonne-france']280.7%
 
['castelnaudary-aude-france']150.4%
 
['saint-pierre-du-mont-landes-france', 'saint-pierre-du-mont-landes-france']150.4%
 
['carros-alpes-maritimes-france']140.4%
 
['saint-chamas-bouches-du-rhone-france']130.3%
 
['limoges-haute-vienne-france', 'limoges-haute-vienne-france']110.3%
 
['camaret-sur-aigues-vaucluse-france', 'camaret-sur-aigues-vaucluse-france']110.3%
 
Other values (138)3348.4%
 
(Missing)151938.2%
 
2020-10-08T12:54:07.762639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique63 ?
Unique (%)2.6%
2020-10-08T12:54:07.957372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length158
Median length3
Mean length7.96908771
Min length2

O_manufacturing_places
Categorical

HIGH CARDINALITY
MISSING

Distinct163
Distinct (%)21.6%
Missing3226
Missing (%)81.1%
Memory size31.1 KiB
France
217 
Italie
164 
Belgique
35 
Espagne
 
28
Provence,France
 
24
Other values (158)
285 
ValueCountFrequency (%) 
France2175.5%
 
Italie1644.1%
 
Belgique350.9%
 
Espagne280.7%
 
Provence,France240.6%
 
Raynal et Roquelaure Provence - R&R (Filiale Cofigeo) - Chemin de Piolenc - 84850 Camaret-sur-Aigues,Vaucluse,Provence-Alpes-Côte d'Azur,France140.4%
 
Royaume-Uni100.3%
 
Thaïlande90.2%
 
France,Provence90.2%
 
Malaisie80.2%
 
Other values (153)2355.9%
 
(Missing)322681.1%
 
2020-10-08T12:54:08.145535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique115 ?
Unique (%)15.3%
2020-10-08T12:54:08.323702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length295
Median length3
Mean length6.076149786
Min length2

O_purchase_places
Categorical

HIGH CARDINALITY
MISSING

Distinct272
Distinct (%)23.2%
Missing2809
Missing (%)70.6%
Memory size31.1 KiB
France
400 
Lyon,France
107 
Paris,France
70 
Courrières,France
 
31
France,Nantes
 
29
Other values (267)
533 
ValueCountFrequency (%) 
France40010.1%
 
Lyon,France1072.7%
 
Paris,France701.8%
 
Courrières,France310.8%
 
France,Nantes290.7%
 
Saint-Priest,France210.5%
 
France,Nantes,Carquefou210.5%
 
Rennes,France160.4%
 
Paris150.4%
 
Italia130.3%
 
Other values (262)44711.2%
 
(Missing)280970.6%
 
2020-10-08T12:54:08.511830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique194 ?
Unique (%)16.6%
2020-10-08T12:54:08.706142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length44
Median length3
Mean length5.658456899
Min length1

O_countries_tags
Categorical

HIGH CARDINALITY
MISSING

Distinct107
Distinct (%)3.1%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
['en:france']
2898 
['en:belgium', 'en:france']
 
97
['en:france', 'en:spain']
 
64
['en:france', 'en:switzerland']
 
64
['en:france', 'en:germany']
 
34
Other values (102)
 
277
ValueCountFrequency (%) 
['en:france']289872.8%
 
['en:belgium', 'en:france']972.4%
 
['en:france', 'en:spain']641.6%
 
['en:france', 'en:switzerland']641.6%
 
['en:france', 'en:germany']340.9%
 
['en:france', 'en:germany', 'en:switzerland']330.8%
 
['en:italy']270.7%
 
['en:france', 'en:italy']210.5%
 
['en:france', 'en:united-kingdom']170.4%
 
['en:france', 'en:united-states']140.4%
 
Other values (97)1654.1%
 
(Missing)54513.7%
 
2020-10-08T12:54:08.910142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique72 ?
Unique (%)2.1%
2020-10-08T12:54:09.156332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length150
Median length13
Mean length14.37220407
Min length3

O_categories
Categorical

HIGH CARDINALITY
MISSING

Distinct755
Distinct (%)25.2%
Missing984
Missing (%)24.7%
Memory size31.1 KiB
Epicerie, Sauces, Sauces tomate
 
183
Epicerie, Sauces
 
179
Epicerie, Sauces, Sauces tomate, Ketchup
 
159
Epicerie, Produits à la viande, Sauces, Sauces à la viande, Sauces pour pâtes, Sauces bolognaises
 
107
Epicerie, Sauces, Sauces Pesto, Pestos au basilic
 
59
Other values (750)
2308 
ValueCountFrequency (%) 
Epicerie, Sauces, Sauces tomate1834.6%
 
Epicerie, Sauces1794.5%
 
Epicerie, Sauces, Sauces tomate, Ketchup1594.0%
 
Epicerie, Produits à la viande, Sauces, Sauces à la viande, Sauces pour pâtes, Sauces bolognaises1072.7%
 
Epicerie, Sauces, Sauces Pesto, Pestos au basilic591.5%
 
Epicerie, Sauces, Sauces barbecue501.3%
 
Epicerie, Condiments, Assaisonnements481.2%
 
Epicerie, Sauces, Sauces tomate, Sauces tomates au basilic431.1%
 
Aliments et boissons à base de végétaux, Aliments d'origine végétale, Aliments à base de fruits et de légumes, Légumes et dérivés, Tomates et dérivés, Concentrés de tomates431.1%
 
Aliments et boissons à base de végétaux, Aliments d'origine végétale, Pickles, Produits de l'olivier, Pickles d'origine végétale, Olives, Olives vertes, Olives dénoyautées, Olives vertes dénoyautées411.0%
 
Other values (745)208352.3%
 
(Missing)98424.7%
 
2020-10-08T12:54:09.347132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique514 ?
Unique (%)17.2%
2020-10-08T12:54:09.532933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length372
Median length40
Mean length63.69741141
Min length3

O_category_properties
Categorical

HIGH CARDINALITY
MISSING

Distinct124
Distinct (%)3.6%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
{}
1952 
{'ciqual_food_name:fr': 'Ketchup', 'ciqual_food_name:en': 'Ketchup'}
 
118
{'ciqual_food_name:en': 'Ketchup', 'ciqual_food_name:fr': 'Ketchup'}
 
112
{'ciqual_food_name:fr': 'Sauce tomate à la viande ou Sauce bolognaise, préemballée', 'ciqual_food_name:en': 'Tomato sauce, with meat or Bolognese sauce, prepacked'}
 
80
{'ciqual_food_name:fr': 'Sauce pesto, préemballée', 'ciqual_food_name:en': 'Sauce, pesto, prepacked'}
 
75
Other values (119)
1097 
ValueCountFrequency (%) 
{}195249.1%
 
{'ciqual_food_name:fr': 'Ketchup', 'ciqual_food_name:en': 'Ketchup'}1183.0%
 
{'ciqual_food_name:en': 'Ketchup', 'ciqual_food_name:fr': 'Ketchup'}1122.8%
 
{'ciqual_food_name:fr': 'Sauce tomate à la viande ou Sauce bolognaise, préemballée', 'ciqual_food_name:en': 'Tomato sauce, with meat or Bolognese sauce, prepacked'}802.0%
 
{'ciqual_food_name:fr': 'Sauce pesto, préemballée', 'ciqual_food_name:en': 'Sauce, pesto, prepacked'}751.9%
 
{'ciqual_food_name:en': 'Sauce, pesto, prepacked', 'ciqual_food_name:fr': 'Sauce pesto, préemballée'}651.6%
 
{'ciqual_food_name:fr': 'Foie gras, canard, bloc -aliment moyen-', 'ciqual_food_name:en': 'Foie gras, block -average-'}481.2%
 
{'ciqual_food_name:en': 'Tomato sauce, with meat or Bolognese sauce, prepacked', 'ciqual_food_name:fr': 'Sauce tomate à la viande ou Sauce bolognaise, préemballée'}481.2%
 
{'ciqual_food_name:en': 'Sauce, pepper, prepacked'}401.0%
 
{'ciqual_food_name:en': 'Foie gras, block -average-', 'ciqual_food_name:fr': 'Foie gras, canard, bloc -aliment moyen-'}391.0%
 
Other values (114)85721.5%
 
(Missing)54513.7%
 
2020-10-08T12:54:09.722407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique33 ?
Unique (%)1.0%
2020-10-08T12:54:10.028355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length230
Median length3
Mean length40.74616738
Min length2

O_pnns_groups_1
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)0.3%
Missing546
Missing (%)13.7%
Memory size31.1 KiB
Fat and sauces
1770 
Salty snacks
831 
unknown
594 
Composite foods
 
104
Fruits and vegetables
 
80
Other values (5)
 
54
ValueCountFrequency (%) 
Fat and sauces177044.5%
 
Salty snacks83120.9%
 
unknown59414.9%
 
Composite foods1042.6%
 
Fruits and vegetables802.0%
 
Fish Meat Eggs290.7%
 
Cereals and potatoes200.5%
 
Beverages30.1%
 
Sugary snacks1< 0.1%
 
Milk and dairy products1< 0.1%
 
(Missing)54613.7%
 
2020-10-08T12:54:10.184615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-10-08T12:54:10.291971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:10.765612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length14
Mean length11.22317165
Min length3

O_pnns_groups_2
Categorical

HIGH CORRELATION
MISSING

Distinct20
Distinct (%)0.6%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
Dressings and sauces
1767 
Salty and fatty products
828 
unknown
594 
One-dish meals
 
81
Vegetables
 
78
Other values (15)
 
86
ValueCountFrequency (%) 
Dressings and sauces176744.4%
 
Salty and fatty products82820.8%
 
unknown59414.9%
 
One-dish meals812.0%
 
Vegetables782.0%
 
Fish and seafood270.7%
 
Cereals200.5%
 
Sandwiches170.4%
 
Pizza pies and quiche60.2%
 
Fats30.1%
 
Other values (10)130.3%
 
(Missing)54513.7%
 
2020-10-08T12:54:11.027582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)0.2%
2020-10-08T12:54:11.496413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length20
Mean length16.06785625
Min length3

O_compared_to_category
Categorical

HIGH CARDINALITY
MISSING

Distinct240
Distinct (%)8.1%
Missing1009
Missing (%)25.4%
Memory size31.1 KiB
en:sauces
271 
en:tomato-sauces
240 
en:ketchup
 
202
en:bolognese-sauces
 
128
en:green-pestos
 
80
Other values (235)
2049 
ValueCountFrequency (%) 
en:sauces2716.8%
 
en:tomato-sauces2406.0%
 
en:ketchup2025.1%
 
en:bolognese-sauces1283.2%
 
en:green-pestos802.0%
 
en:seasonings751.9%
 
en:pestos641.6%
 
en:green-pitted-olives631.6%
 
en:tomato-pastes621.6%
 
en:barbecue-sauces571.4%
 
Other values (230)172843.4%
 
(Missing)100925.4%
 
2020-10-08T12:54:11.655730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique70 ?
Unique (%)2.4%
2020-10-08T12:54:11.819767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length55
Median length15
Mean length13.5433526
Min length3

O_interface_version_modified
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing549
Missing (%)13.8%
Memory size31.1 KiB
20150316.jqm2
2571 
20120622
608 
20190830
 
249
20130323.jqm
 
2
ValueCountFrequency (%) 
20150316.jqm2257164.6%
 
2012062260815.3%
 
201908302496.3%
 
20130323.jqm20.1%
 
(Missing)54913.8%
 
2020-10-08T12:54:12.106514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:12.187768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:12.287648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length10.54284996
Min length3

O_ingredients_n
Real number (ℝ≥0)

MISSING

Distinct75
Distinct (%)3.0%
Missing1437
Missing (%)36.1%
Infinite0
Infinite (%)0.0%
Mean15.55782848
Minimum0
Maximum131
Zeros1
Zeros (%)< 0.1%
Memory size31.1 KiB
2020-10-08T12:54:12.412857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median14
Q319
95-th percentile35
Maximum131
Range131
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.92443004
Coefficient of variation (CV)0.7021821877
Kurtosis14.69292705
Mean15.55782848
Median Absolute Deviation (MAD)5
Skewness2.70503301
Sum39548
Variance119.3431717
MonotocityNot monotonic
2020-10-08T12:54:12.580200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
121463.7%
 
111363.4%
 
141343.4%
 
161313.3%
 
101243.1%
 
91223.1%
 
131223.1%
 
81162.9%
 
71152.9%
 
171122.8%
 
Other values (65)128432.3%
 
(Missing)143736.1%
 
ValueCountFrequency (%) 
01< 0.1%
 
1531.3%
 
2381.0%
 
3411.0%
 
4691.7%
 
ValueCountFrequency (%) 
1311< 0.1%
 
1061< 0.1%
 
971< 0.1%
 
941< 0.1%
 
931< 0.1%
 

O_ingredients_text
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct2466
Distinct (%)97.0%
Missing1437
Missing (%)36.1%
Memory size31.1 KiB
Tomates
 
5
Pulpe de tomates 72%, concentré de tomates 14%, oignons, huile de tournesol, basilic 2%, sucre, sel, arôme naturel.
 
5
Pulpe de tomate et concentré de tomate 54%, viande 18,5% (bœuf cuit 9.5%, porc), eau, légumes (oignons, carottes), sel, sucre, amidon transformé, plante aromatique, arômes, huile de tournesol, ail.
 
4
Tomates (148 g pour 100 g de tomato ketchup), vinaigre, sucre, sel, extraits d'épices et d'herbes (contiennent du _céleri_), épice.
 
4
Tomates (148 g pour 100 g de Tomato Ketchup), vinaigre, sucre, sel, extraits d'épices et d'herbes (contiennent du _céleri_), épice.
 
3
Other values (2461)
2521 
ValueCountFrequency (%) 
Tomates50.1%
 
Pulpe de tomates 72%, concentré de tomates 14%, oignons, huile de tournesol, basilic 2%, sucre, sel, arôme naturel.50.1%
 
Pulpe de tomate et concentré de tomate 54%, viande 18,5% (bœuf cuit 9.5%, porc), eau, légumes (oignons, carottes), sel, sucre, amidon transformé, plante aromatique, arômes, huile de tournesol, ail.40.1%
 
Tomates (148 g pour 100 g de tomato ketchup), vinaigre, sucre, sel, extraits d'épices et d'herbes (contiennent du _céleri_), épice.40.1%
 
Tomates (148 g pour 100 g de Tomato Ketchup), vinaigre, sucre, sel, extraits d'épices et d'herbes (contiennent du _céleri_), épice.30.1%
 
Poivre gris moulu Traces éventuelles de céleri et moutarde.30.1%
 
Purée de tomates double concentrée 99%, sel.30.1%
 
Pulpe de tomates 65%, eau, concentré de tomates 9%, oignons 8%, huile de tournesol, sel, sucre, persil 0,6%, thym 0,4%, ail, origan 0,08%, poivre noir.30.1%
 
Viande de porc 91%, gras de porc, sel, poivre.30.1%
 
purée de tomates 50% (soit 140g de tomates pour 100g de produit fini), eau, sucre, vinaigre d'alcool, sel, arôme naturel d'épices. Traces éventuelles de céleri, lait et moutarde.30.1%
 
Other values (2456)250663.0%
 
(Missing)143736.1%
 
2020-10-08T12:54:12.775081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2411 ?
Unique (%)94.8%
2020-10-08T12:54:12.960982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2127
Median length130
Mean length170.7532043
Min length3

O_ingredients
Categorical

HIGH CARDINALITY
MISSING

Distinct2376
Distinct (%)72.7%
Missing712
Missing (%)17.9%
Memory size31.1 KiB
[]
726 
['Tomates', 'vinaigre', 'sucre', 'sel', "extraits d'épices et d'herbes", 'épice', 'contiennent du _céleri_']
 
10
['Viande de porc', 'gras de porc', 'sel', 'poivre']
 
9
['Tomates']
 
9
['Tomates', 'sel']
 
8
Other values (2371)
2505 
ValueCountFrequency (%) 
[]72618.2%
 
['Tomates', 'vinaigre', 'sucre', 'sel', "extraits d'épices et d'herbes", 'épice', 'contiennent du _céleri_']100.3%
 
['Viande de porc', 'gras de porc', 'sel', 'poivre']90.2%
 
['Tomates']90.2%
 
['Tomates', 'sel']80.2%
 
['Tomates', 'vinaigre', 'sucre', 'sel', "extraits d'épices et d'herbes", 'épice', 'contiennent du céleri']60.2%
 
['Pulpe de tomates', 'concentré de tomates', 'oignons', 'huile de tournesol', 'basilic', 'sucre', 'sel', 'arôme naturel']50.1%
 
['Viande de porc', 'gras de porc', 'sel de Guérande', 'poivre']50.1%
 
['Purée de tomate à base de concentré', 'pulpe de tomate', 'viande de porc', 'oignon', 'viande de bœuf', 'carottes', 'huile de colza', 'sucre', 'amidon modifié de pomme de terre', 'sel', 'céleri', 'acidifiant', 'épices et plantes aromatiques', 'arôme de bœuf', 'ail', 'persil', 'poivre', 'tomate pelée', 'jus de tomate', 'acidifiant', 'équivaut à 10% de viande crue', 'équivaut à 10% de viande crue', 'acide lactique', 'acide citrique']40.1%
 
['Olives', 'eau', 'sel', 'acidifiant', 'acide citrique']40.1%
 
Other values (2366)248162.4%
 
(Missing)71217.9%
 
2020-10-08T12:54:13.145543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2270 ?
Unique (%)69.5%
2020-10-08T12:54:13.360341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2164
Median length127
Mean length166.2410153
Min length2

O_salt_content
Categorical

HIGH CARDINALITY
MISSING

Distinct1842
Distinct (%)56.4%
Missing712
Missing (%)17.9%
Memory size31.1 KiB
[]
1181 
[{'vegetarian': 'yes', 'vegan': 'yes', 'rank': 4, 'id': 'en:salt', 'text': 'sel'}]
 
13
[{'vegan': 'yes', 'rank': 6, 'vegetarian': 'yes', 'text': 'sel', 'id': 'en:salt'}]
 
10
[{'text': 'sel', 'id': 'en:salt', 'rank': 6, 'vegan': 'yes', 'vegetarian': 'yes'}]
 
10
[{'text': 'sel', 'id': 'en:salt', 'vegan': 'yes', 'rank': 4, 'vegetarian': 'yes'}]
 
9
Other values (1837)
2044 
ValueCountFrequency (%) 
[]118129.7%
 
[{'vegetarian': 'yes', 'vegan': 'yes', 'rank': 4, 'id': 'en:salt', 'text': 'sel'}]130.3%
 
[{'vegan': 'yes', 'rank': 6, 'vegetarian': 'yes', 'text': 'sel', 'id': 'en:salt'}]100.3%
 
[{'text': 'sel', 'id': 'en:salt', 'rank': 6, 'vegan': 'yes', 'vegetarian': 'yes'}]100.3%
 
[{'text': 'sel', 'id': 'en:salt', 'vegan': 'yes', 'rank': 4, 'vegetarian': 'yes'}]90.2%
 
[{'vegan': 'yes', 'rank': 4, 'vegetarian': 'yes', 'text': 'sel', 'id': 'en:salt'}]90.2%
 
[{'vegan': 'yes', 'rank': 7, 'vegetarian': 'yes', 'text': 'sel', 'id': 'en:salt'}]70.2%
 
[{'text': 'sel', 'id': 'en:salt', 'rank': 4, 'vegan': 'yes', 'vegetarian': 'yes'}]70.2%
 
[{'vegetarian': 'yes', 'rank': 8, 'vegan': 'yes', 'id': 'en:salt', 'text': 'sel'}]70.2%
 
[{'rank': 5, 'vegan': 'yes', 'vegetarian': 'yes', 'text': 'sel', 'id': 'en:salt'}]60.2%
 
Other values (1832)200850.5%
 
(Missing)71217.9%
 
2020-10-08T12:54:13.544173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1734 ?
Unique (%)53.1%
2020-10-08T12:54:13.731908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length799
Median length82
Mean length80.53531038
Min length2

O_max_salt_content
Real number (ℝ≥0)

MISSING
ZEROS

Distinct647
Distinct (%)18.8%
Missing545
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean5.332230854
Minimum0
Maximum100
Zeros1807
Zeros (%)45.4%
Memory size31.1 KiB
2020-10-08T12:54:13.903475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.38595
95-th percentile28.09333333
Maximum100
Range100
Interquartile range (IQR)5.38595

Descriptive statistics

Standard deviation10.74547492
Coefficient of variation (CV)2.015193118
Kurtosis14.68458651
Mean5.332230854
Median Absolute Deviation (MAD)0
Skewness3.313074516
Sum18310.88075
Variance115.4652313
MonotocityNot monotonic
2020-10-08T12:54:14.134029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0180745.4%
 
2621.6%
 
50611.5%
 
33.33333333581.5%
 
20571.4%
 
1491.2%
 
10441.1%
 
25441.1%
 
5360.9%
 
16.66666667340.9%
 
Other values (637)118229.7%
 
(Missing)54513.7%
 
ValueCountFrequency (%) 
0180745.4%
 
0.011< 0.1%
 
0.11< 0.1%
 
0.21< 0.1%
 
0.21< 0.1%
 
ValueCountFrequency (%) 
10050.1%
 
74.11< 0.1%
 
66.21< 0.1%
 
50611.5%
 
49.851< 0.1%
 

O_min_salt_content
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct137
Distinct (%)4.0%
Missing545
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean0.2089924032
Minimum0
Maximum50
Zeros2962
Zeros (%)74.4%
Memory size31.1 KiB
2020-10-08T12:54:14.414795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.346590156
Coefficient of variation (CV)6.443249301
Kurtosis627.5555259
Mean0.2089924032
Median Absolute Deviation (MAD)0
Skewness20.57410725
Sum717.6799126
Variance1.813305048
MonotocityNot monotonic
2020-10-08T12:54:14.600791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0296274.4%
 
1491.2%
 
0.5431.1%
 
0.4250.6%
 
0.3250.6%
 
0.1240.6%
 
0.2220.6%
 
0.8200.5%
 
0.6120.3%
 
0.7120.3%
 
Other values (127)2406.0%
 
(Missing)54513.7%
 
ValueCountFrequency (%) 
0296274.4%
 
0.00920.1%
 
0.011< 0.1%
 
0.0260.2%
 
0.0320.1%
 
ValueCountFrequency (%) 
501< 0.1%
 
25.91< 0.1%
 
251< 0.1%
 
13.41< 0.1%
 
10.633333331< 0.1%
 

O_oil_content
Categorical

HIGH CARDINALITY
MISSING

Distinct1168
Distinct (%)35.8%
Missing712
Missing (%)17.9%
Memory size31.1 KiB
[]
2067 
[{'vegetarian': 'yes', 'vegan': 'yes', 'from_palm_oil': 'no', 'id': 'en:sunflower-oil', 'text': 'huile de tournesol'}]
 
3
[{'vegan': 'yes', 'rank': 5, 'vegetarian': 'yes', 'from_palm_oil': 'no', 'text': 'huile de tournesol', 'id': 'en:sunflower-oil'}]
 
3
[{'from_palm_oil': 'no', 'vegetarian': 'yes', 'rank': 10, 'vegan': 'yes', 'id': 'en:sunflower-oil', 'text': 'huile de tournesol'}]
 
3
[{'text': 'huile de tournesol', 'id': 'en:sunflower-oil', 'from_palm_oil': 'no', 'vegan': 'yes', 'rank': 12, 'vegetarian': 'yes'}]
 
2
Other values (1163)
1189 
ValueCountFrequency (%) 
[]206751.9%
 
[{'vegetarian': 'yes', 'vegan': 'yes', 'from_palm_oil': 'no', 'id': 'en:sunflower-oil', 'text': 'huile de tournesol'}]30.1%
 
[{'vegan': 'yes', 'rank': 5, 'vegetarian': 'yes', 'from_palm_oil': 'no', 'text': 'huile de tournesol', 'id': 'en:sunflower-oil'}]30.1%
 
[{'from_palm_oil': 'no', 'vegetarian': 'yes', 'rank': 10, 'vegan': 'yes', 'id': 'en:sunflower-oil', 'text': 'huile de tournesol'}]30.1%
 
[{'text': 'huile de tournesol', 'id': 'en:sunflower-oil', 'from_palm_oil': 'no', 'vegan': 'yes', 'rank': 12, 'vegetarian': 'yes'}]20.1%
 
[{'from_palm_oil': 'no', 'vegan': 'yes', 'vegetarian': 'yes', 'text': 'huile de tournesol', 'id': 'en:sunflower-oil'}]20.1%
 
[{'from_palm_oil': 'no', 'percent_min': 1, 'rank': 3, 'text': 'huile de tournesol', 'id': 'en:sunflower-oil', 'vegan': 'yes', 'percent_max': 7, 'vegetarian': 'yes'}]20.1%
 
[{'percent_min': 0, 'from_palm_oil': 'no', 'vegetarian': 'yes', 'vegan': 'yes', 'percent_max': 4.375, 'id': 'en:olive-oil', 'text': "huile d'olive"}]20.1%
 
[{'from_palm_oil': 'no', 'vegetarian': 'yes', 'rank': 6, 'vegan': 'yes', 'id': 'en:sunflower-oil', 'text': 'huile de tournesol'}]20.1%
 
[{'vegetarian': 'yes', 'vegan': 'yes', 'rank': 4, 'from_palm_oil': 'no', 'id': 'en:sunflower-oil', 'text': 'huile de tournesol'}]20.1%
 
Other values (1158)117929.6%
 
(Missing)71217.9%
 
2020-10-08T12:54:14.777638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1137 ?
Unique (%)34.8%
2020-10-08T12:54:14.953844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length756
Median length2
Mean length61.89042473
Min length2

O_max_oil_content
Real number (ℝ≥0)

MISSING
ZEROS

Distinct427
Distinct (%)12.4%
Missing545
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean6.201473135
Minimum0
Maximum100
Zeros2488
Zeros (%)62.5%
Memory size31.1 KiB
2020-10-08T12:54:15.120770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.921428571
95-th percentile41.5875
Maximum100
Range100
Interquartile range (IQR)1.921428571

Descriptive statistics

Standard deviation16.4308837
Coefficient of variation (CV)2.649513001
Kurtosis13.39953971
Mean6.201473135
Median Absolute Deviation (MAD)0
Skewness3.529811238
Sum21295.85875
Variance269.9739391
MonotocityNot monotonic
2020-10-08T12:54:15.279233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0248862.5%
 
100270.7%
 
6250.6%
 
33.33333333240.6%
 
10210.5%
 
20200.5%
 
4200.5%
 
7160.4%
 
2150.4%
 
50140.4%
 
Other values (417)76419.2%
 
(Missing)54513.7%
 
ValueCountFrequency (%) 
0248862.5%
 
0.022222222221< 0.1%
 
0.220.1%
 
0.320.1%
 
0.41< 0.1%
 
ValueCountFrequency (%) 
100270.7%
 
98.21< 0.1%
 
93.51< 0.1%
 
93.21< 0.1%
 
92.81< 0.1%
 

O_min_oil_content
Real number (ℝ≥0)

MISSING
ZEROS

Distinct265
Distinct (%)7.7%
Missing545
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean1.57327059
Minimum0
Maximum90
Zeros2801
Zeros (%)70.4%
Memory size31.1 KiB
2020-10-08T12:54:15.437782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.8
Maximum90
Range90
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.070739443
Coefficient of variation (CV)3.858674713
Kurtosis46.84884299
Mean1.57327059
Median Absolute Deviation (MAD)0
Skewness6.049447676
Sum5402.611207
Variance36.85387738
MonotocityNot monotonic
2020-10-08T12:54:15.610521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0280170.4%
 
2340.9%
 
1180.5%
 
15170.4%
 
5140.4%
 
20130.3%
 
0.8130.3%
 
3120.3%
 
0.3120.3%
 
0.1110.3%
 
Other values (255)48912.3%
 
(Missing)54513.7%
 
ValueCountFrequency (%) 
0280170.4%
 
0.021< 0.1%
 
0.041< 0.1%
 
0.0530.1%
 
0.057142857141< 0.1%
 
ValueCountFrequency (%) 
901< 0.1%
 
6620.1%
 
611< 0.1%
 
601< 0.1%
 
591< 0.1%
 

O_oil_type
Categorical

MISSING

Distinct18
Distinct (%)1.9%
Missing3031
Missing (%)76.2%
Memory size31.1 KiB
huile de tournesol
479 
huile de colza
181 
Huile de colza
74 
Huile de tournesol
65 
huile d'olive
57 
Other values (13)
92 
ValueCountFrequency (%) 
huile de tournesol47912.0%
 
huile de colza1814.5%
 
Huile de colza741.9%
 
Huile de tournesol651.6%
 
huile d'olive571.4%
 
huile d'olive vierge extra extraite à froid170.4%
 
huile d'olive extra vierge130.3%
 
Sonnenblumenöl120.3%
 
huile d'olive vierge90.2%
 
huile d'olive extra-vierge90.2%
 
Other values (8)320.8%
 
(Missing)303176.2%
 
2020-10-08T12:54:15.762005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:15.902170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length43
Median length3
Mean length6.44282483
Min length3

O_nutriments
Categorical

HIGH CARDINALITY
MISSING

Distinct3349
Distinct (%)97.5%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
{}
 
79
{'sugars_value': 5.5, 'energy': 276, 'energy_100g': 276, 'energy-kcal': 66, 'salt': 1.1, 'sodium': 0.44000000000000006, 'sodium_100g': 0.44000000000000006, 'energy-kcal_value': 66, 'salt_unit': 'g', 'sugars_100g': 5.5, 'sugars_unit': 'g', 'fat_unit': 'g', 'salt_value': 1.1, 'sugars': 5.5, 'energy-kcal_unit': 'kcal', 'proteins_value': 4.9, 'fat_value': 1.9, 'carbohydrates_value': 6.4, 'energy-kcal_100g': 66, 'proteins': 4.9, 'energy_value': 66, 'proteins_unit': 'g', 'carbohydrates_100g': 6.4, 'energy_unit': 'kcal', 'sodium_value': 0.44000000000000006, 'salt_100g': 1.1, 'fat': 1.9, 'proteins_100g': 4.9, 'carbohydrates': 6.4, 'saturated-fat_unit': 'g', 'saturated-fat_value': 0.7, 'fat_100g': 1.9, 'sodium_unit': 'g', 'saturated-fat_100g': 0.7, 'carbohydrates_unit': 'g', 'saturated-fat': 0.7}
 
2
{'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'nova-group_100g': 3, 'nova-group_serving': 3, 'nova-group': 3}
 
2
{'sugars_unit': '', 'salt_unit': '', 'sugars_100g': 5.1, 'energy-kcal_value': 70, 'sodium_100g': 0.48, 'nutrition-score-fr': 4, 'salt': 1.2, 'sodium': 0.48, 'sugars_value': 5.1, 'energy': 293, 'energy_100g': 293, 'energy-kcal': 70, 'saturated-fat': 0.9, 'nutrition-score-fr_serving': 4, 'sodium_unit': 'g', 'saturated-fat_100g': 0.9, 'carbohydrates_unit': '', 'saturated-fat_unit': '', 'saturated-fat_value': 0.9, 'nutrition-score-fr_100g': 4, 'fat_100g': 2.4, 'proteins_100g': 4.6, 'carbohydrates': 7.1, 'energy_unit': 'kcal', 'sodium_value': 0.48, 'salt_100g': 1.2, 'fat': 2.4, 'proteins': 4.6, 'energy_value': 70, 'proteins_unit': '', 'carbohydrates_100g': 7.1, 'energy-kcal_100g': 70, 'fat_unit': '', 'salt_value': 1.2, 'sugars': 5.1, 'energy-kcal_unit': 'kcal', 'fat_value': 2.4, 'proteins_value': 4.6, 'carbohydrates_value': 7.1}
 
2
{'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0}
 
2
Other values (3344)
3347 
ValueCountFrequency (%) 
{}792.0%
 
{'sugars_value': 5.5, 'energy': 276, 'energy_100g': 276, 'energy-kcal': 66, 'salt': 1.1, 'sodium': 0.44000000000000006, 'sodium_100g': 0.44000000000000006, 'energy-kcal_value': 66, 'salt_unit': 'g', 'sugars_100g': 5.5, 'sugars_unit': 'g', 'fat_unit': 'g', 'salt_value': 1.1, 'sugars': 5.5, 'energy-kcal_unit': 'kcal', 'proteins_value': 4.9, 'fat_value': 1.9, 'carbohydrates_value': 6.4, 'energy-kcal_100g': 66, 'proteins': 4.9, 'energy_value': 66, 'proteins_unit': 'g', 'carbohydrates_100g': 6.4, 'energy_unit': 'kcal', 'sodium_value': 0.44000000000000006, 'salt_100g': 1.1, 'fat': 1.9, 'proteins_100g': 4.9, 'carbohydrates': 6.4, 'saturated-fat_unit': 'g', 'saturated-fat_value': 0.7, 'fat_100g': 1.9, 'sodium_unit': 'g', 'saturated-fat_100g': 0.7, 'carbohydrates_unit': 'g', 'saturated-fat': 0.7}20.1%
 
{'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'nova-group_100g': 3, 'nova-group_serving': 3, 'nova-group': 3}20.1%
 
{'sugars_unit': '', 'salt_unit': '', 'sugars_100g': 5.1, 'energy-kcal_value': 70, 'sodium_100g': 0.48, 'nutrition-score-fr': 4, 'salt': 1.2, 'sodium': 0.48, 'sugars_value': 5.1, 'energy': 293, 'energy_100g': 293, 'energy-kcal': 70, 'saturated-fat': 0.9, 'nutrition-score-fr_serving': 4, 'sodium_unit': 'g', 'saturated-fat_100g': 0.9, 'carbohydrates_unit': '', 'saturated-fat_unit': '', 'saturated-fat_value': 0.9, 'nutrition-score-fr_100g': 4, 'fat_100g': 2.4, 'proteins_100g': 4.6, 'carbohydrates': 7.1, 'energy_unit': 'kcal', 'sodium_value': 0.48, 'salt_100g': 1.2, 'fat': 2.4, 'proteins': 4.6, 'energy_value': 70, 'proteins_unit': '', 'carbohydrates_100g': 7.1, 'energy-kcal_100g': 70, 'fat_unit': '', 'salt_value': 1.2, 'sugars': 5.1, 'energy-kcal_unit': 'kcal', 'fat_value': 2.4, 'proteins_value': 4.6, 'carbohydrates_value': 7.1}20.1%
 
{'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0}20.1%
 
{'nova-group_serving': 1, 'nova-group': 1, 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'nova-group_100g': 1}20.1%
 
{'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'nova-group_100g': 4, 'nova-group': 4, 'nova-group_serving': 4}20.1%
 
{'sodium_unit': 'g', 'carbohydrates_unit': '', 'saturated-fat_100g': 0.9, 'saturated-fat': 0.9, 'proteins_100g': 4.6, 'carbohydrates': 7.1, 'saturated-fat_unit': '', 'saturated-fat_value': 0.9, 'fat_100g': 2.4, 'proteins': 4.6, 'energy_value': 70, 'proteins_unit': '', 'carbohydrates_100g': 7.1, 'energy_unit': 'kcal', 'sodium_value': 0.48, 'salt_100g': 1.2, 'fat': 2.4, 'fat_unit': '', 'salt_value': 1.2, 'sugars': 5.1, 'energy-kcal_unit': 'kcal', 'carbohydrates_value': 7.1, 'fat_value': 2.4, 'proteins_value': 4.6, 'energy-kcal_100g': 70, 'sugars_unit': '', 'sugars_100g': 5.1, 'salt_unit': '', 'sodium_100g': 0.48, 'energy-kcal_value': 70, 'sugars_value': 5.1, 'energy': 293, 'energy_100g': 293, 'energy-kcal': 70, 'salt': 1.2, 'sodium': 0.48}20.1%
 
{'energy-kcal': 161, 'energy_100g': 674, 'energy': 674, 'sugars_value': 0.5, 'sodium': 1.24, 'salt': 3.1, 'energy-kcal_value': 161, 'sodium_100g': 1.24, 'nutrition-score-fr': 14, 'carbon-footprint-from-known-ingredients_100g': 92.00999999999999, 'nova-group': 3, 'nova-group_100g': 3, 'salt_unit': 'g', 'sugars_100g': 0.5, 'sugars_unit': 'g', 'sugars': 0.5, 'energy-kcal_unit': 'kcal', 'carbohydrates_value': 1.3, 'fat_value': 15.8, 'proteins_value': 1.2, 'fat_unit': 'g', 'salt_value': 3.1, 'nova-group_serving': 3, 'energy-kcal_100g': 161, 'proteins_unit': 'g', 'carbohydrates_100g': 1.3, 'proteins': 1.2, 'energy_value': 161, 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 2.0999999999999996, 'fat': 15.8, 'energy_unit': 'kcal', 'carbon-footprint-from-known-ingredients_product': 230, 'sodium_value': 1.24, 'salt_100g': 3.1, 'carbohydrates': 1.3, 'proteins_100g': 1.2, 'nutrition-score-fr_100g': 14, 'fat_100g': 15.8, 'saturated-fat_unit': 'g', 'saturated-fat_value': 2.5, 'carbohydrates_unit': 'g', 'saturated-fat_100g': 2.5, 'sodium_unit': 'g', 'nutrition-score-fr_serving': 14, 'saturated-fat': 2.5}1< 0.1%
 
{'nutrition-score-fr_serving': 6, 'saturated-fat': 4.1, 'carbohydrates_unit': 'g', 'sodium_unit': 'g', 'sugars_serving': 1.6, 'nutrition-score-fr_100g': 6, 'saturated-fat_value': 4.1, 'energy-kcal_serving': 248, 'carbon-footprint-from-known-ingredients_product': 225, 'energy_unit': 'kJ', 'sodium_value': 0.4, 'salt_100g': 1, 'carbohydrates_100g': 11, 'energy_value': 519, 'sugars': 0.8, 'carbohydrates_value': 11, 'proteins_value': 3.4, 'energy_serving': 1040, 'sugars_unit': 'g', 'energy-kj': 519, 'salt_unit': 'g', 'sugars_100g': 0.8, 'fiber_100g': 1, 'fat_serving': 14.4, 'nova-group': 4, 'sodium_100g': 0.4, 'energy-kcal_value': 124, 'energy-kj_unit': 'kJ', 'proteins_serving': 6.8, 'energy-kcal': 124, 'energy_100g': 519, 'saturated-fat_100g': 4.1, 'fat_100g': 7.2, 'saturated-fat_unit': 'g', 'energy-kj_serving': 1040, 'carbohydrates': 11, 'proteins_100g': 3.4, 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'fat': 7.2, 'carbohydrates_serving': 22, 'proteins_unit': 'g', 'sodium_serving': 0.8, 'salt_serving': 2, 'proteins': 3.4, 'fiber_value': 1, 'energy-kcal_100g': 124, 'energy-kcal_unit': 'kcal', 'fat_value': 7.2, 'fat_unit': 'g', 'salt_value': 1, 'nova-group_serving': 4, 'fiber_serving': 2, 'carbon-footprint-from-known-ingredients_serving': 113, 'nova-group_100g': 4, 'saturated-fat_serving': 8.2, 'energy-kj_100g': 519, 'carbon-footprint-from-known-ingredients_100g': 56.35, 'nutrition-score-fr': 6, 'energy-kj_value': 519, 'sodium': 0.4, 'salt': 1, 'energy': 519, 'sugars_value': 0.8, 'fiber': 1, 'fiber_unit': 'g'}1< 0.1%
 
Other values (3339)333983.9%
 
(Missing)54513.7%
 
2020-10-08T12:54:16.081127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3341 ?
Unique (%)97.3%
2020-10-08T12:54:16.269232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3297
Median length1025
Mean length930.2221664
Min length2

O_energy_100g
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1063
Distinct (%)32.1%
Missing670
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean839.6752191
Minimum0
Maximum3766
Zeros13
Zeros (%)0.3%
Memory size31.1 KiB
2020-10-08T12:54:16.508840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile187
Q1339
median582
Q31259
95-th percentile2166
Maximum3766
Range3766
Interquartile range (IQR)920

Descriptive statistics

Standard deviation656.2772955
Coefficient of variation (CV)0.7815846896
Kurtosis0.7129689475
Mean839.6752191
Median Absolute Deviation (MAD)318
Skewness1.148336335
Sum2778485.3
Variance430699.8885
MonotocityNot monotonic
2020-10-08T12:54:16.666795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
427521.3%
 
247360.9%
 
268360.9%
 
435300.8%
 
276280.7%
 
607230.6%
 
389210.5%
 
469210.5%
 
364200.5%
 
418200.5%
 
Other values (1053)302275.9%
 
(Missing)67016.8%
 
ValueCountFrequency (%) 
0130.3%
 
420.1%
 
81< 0.1%
 
131< 0.1%
 
211< 0.1%
 
ValueCountFrequency (%) 
376650.1%
 
37031< 0.1%
 
34641< 0.1%
 
34391< 0.1%
 
31511< 0.1%
 

O_energy_kcal_100g
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct553
Distinct (%)20.5%
Missing1288
Missing (%)32.4%
Infinite0
Infinite (%)0.0%
Mean203.4081618
Minimum0
Maximum1772
Zeros11
Zeros (%)0.3%
Memory size31.1 KiB
2020-10-08T12:54:16.838462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.5
Q182
median142
Q3305
95-th percentile520.5
Maximum1772
Range1772
Interquartile range (IQR)223

Descriptive statistics

Standard deviation160.898485
Coefficient of variation (CV)0.791012925
Kurtosis3.856617034
Mean203.4081618
Median Absolute Deviation (MAD)79
Skewness1.414117877
Sum547371.3633
Variance25888.32248
MonotocityNot monotonic
2020-10-08T12:54:17.272571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
102631.6%
 
64401.0%
 
59350.9%
 
66320.8%
 
145290.7%
 
93250.6%
 
91220.6%
 
77220.6%
 
100210.5%
 
70200.5%
 
Other values (543)238259.9%
 
(Missing)128832.4%
 
ValueCountFrequency (%) 
0110.3%
 
0.931< 0.1%
 
120.1%
 
21< 0.1%
 
31< 0.1%
 
ValueCountFrequency (%) 
17721< 0.1%
 
90050.1%
 
8851< 0.1%
 
8281< 0.1%
 
8221< 0.1%
 

O_saturated_fat_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct275
Distinct (%)8.4%
Missing699
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean3.487313415
Minimum0
Maximum52
Zeros264
Zeros (%)6.6%
Memory size31.1 KiB
2020-10-08T12:54:17.446066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3
median1.6
Q34.1
95-th percentile15
Maximum52
Range52
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation5.282616703
Coefficient of variation (CV)1.514809848
Kurtosis10.81926874
Mean3.487313415
Median Absolute Deviation (MAD)1.5
Skewness2.869022322
Sum11438.388
Variance27.90603923
MonotocityNot monotonic
2020-10-08T12:54:17.622132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.12887.2%
 
02646.6%
 
0.21413.5%
 
0.31293.2%
 
0.41172.9%
 
0.5932.3%
 
1751.9%
 
3681.7%
 
2641.6%
 
0.7551.4%
 
Other values (265)198649.9%
 
(Missing)69917.6%
 
ValueCountFrequency (%) 
02646.6%
 
0.0011< 0.1%
 
0.0140.1%
 
0.0230.1%
 
0.0330.1%
 
ValueCountFrequency (%) 
521< 0.1%
 
501< 0.1%
 
431< 0.1%
 
401< 0.1%
 
351< 0.1%
 

O_fat_100g
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct487
Distinct (%)14.8%
Missing688
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean15.35986327
Minimum0
Maximum100
Zeros103
Zeros (%)2.6%
Memory size31.1 KiB
2020-10-08T12:54:17.799532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q12.1
median7
Q325.1
95-th percentile52.45
Maximum100
Range100
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.61682123
Coefficient of variation (CV)1.146938675
Kurtosis1.104324838
Mean15.35986327
Median Absolute Deviation (MAD)6.75
Skewness1.321153155
Sum50549.31001
Variance310.3523901
MonotocityNot monotonic
2020-10-08T12:54:17.987953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.11594.0%
 
0.51142.9%
 
01032.6%
 
0.2782.0%
 
2.4571.4%
 
1561.4%
 
15431.1%
 
30370.9%
 
14360.9%
 
3.1340.9%
 
Other values (477)257464.7%
 
(Missing)68817.3%
 
ValueCountFrequency (%) 
01032.6%
 
0.0120.1%
 
0.021< 0.1%
 
0.041< 0.1%
 
0.11594.0%
 
ValueCountFrequency (%) 
10040.1%
 
921< 0.1%
 
811< 0.1%
 
8040.1%
 
781< 0.1%
 

O_carbohydrates_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct404
Distinct (%)12.3%
Missing686
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean10.36327867
Minimum0
Maximum87.2
Zeros99
Zeros (%)2.5%
Memory size31.1 KiB
2020-10-08T12:54:18.151911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q14
median6.8
Q311.9
95-th percentile32.24
Maximum87.2
Range87.2
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation11.43114991
Coefficient of variation (CV)1.103043764
Kurtosis8.333454598
Mean10.36327867
Median Absolute Deviation (MAD)3.4
Skewness2.620156894
Sum34126.27667
Variance130.6711883
MonotocityNot monotonic
2020-10-08T12:54:18.302095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0992.5%
 
0.5822.1%
 
23.2661.7%
 
12631.6%
 
7.1571.4%
 
10471.2%
 
6.6461.2%
 
2451.1%
 
6.5431.1%
 
6.4421.1%
 
Other values (394)270367.9%
 
(Missing)68617.2%
 
ValueCountFrequency (%) 
0992.5%
 
0.011< 0.1%
 
0.150.1%
 
0.2130.3%
 
0.2000000031< 0.1%
 
ValueCountFrequency (%) 
87.21< 0.1%
 
841< 0.1%
 
731< 0.1%
 
721< 0.1%
 
7130.1%
 

O_sugars_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct343
Distinct (%)10.4%
Missing693
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean6.511827956
Minimum0
Maximum80
Zeros200
Zeros (%)5.0%
Memory size31.1 KiB
2020-10-08T12:54:18.458574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.4
median4.2
Q36.8
95-th percentile23
Maximum80
Range80
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation8.302695502
Coefficient of variation (CV)1.275017638
Kurtosis9.84925997
Mean6.511827956
Median Absolute Deviation (MAD)2.8
Skewness2.714014524
Sum21397.86666
Variance68.93475259
MonotocityNot monotonic
2020-10-08T12:54:18.616069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02005.0%
 
0.51834.6%
 
22.8681.7%
 
5611.5%
 
4511.3%
 
0.9491.2%
 
6.5461.2%
 
4.2451.1%
 
5.6441.1%
 
0.6441.1%
 
Other values (333)249562.7%
 
(Missing)69317.4%
 
ValueCountFrequency (%) 
02005.0%
 
0.0130.1%
 
0.0240.1%
 
0.0520.1%
 
0.061< 0.1%
 
ValueCountFrequency (%) 
801< 0.1%
 
65.41< 0.1%
 
601< 0.1%
 
591< 0.1%
 
581< 0.1%
 

O_fruits_vegetables_nuts_estimate_from_ingredients_100g
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct730
Distinct (%)28.7%
Missing1438
Missing (%)36.1%
Infinite0
Infinite (%)0.0%
Mean33.3404283
Minimum0
Maximum13449.5
Zeros885
Zeros (%)22.2%
Memory size31.1 KiB
2020-10-08T12:54:18.776327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q352.38
95-th percentile95.37142857
Maximum13449.5
Range13449.5
Interquartile range (IQR)52.38

Descriptive statistics

Standard deviation268.8623256
Coefficient of variation (CV)8.064153322
Kurtosis2443.728701
Mean33.3404283
Median Absolute Deviation (MAD)9
Skewness48.9606071
Sum84718.02832
Variance72286.95015
MonotocityNot monotonic
2020-10-08T12:54:18.943335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
088522.2%
 
10320.8%
 
5210.5%
 
20200.5%
 
4190.5%
 
100190.5%
 
99180.5%
 
50180.5%
 
25150.4%
 
21130.3%
 
Other values (720)148137.2%
 
(Missing)143836.1%
 
ValueCountFrequency (%) 
088522.2%
 
0.011< 0.1%
 
0.041< 0.1%
 
0.051< 0.1%
 
0.120.1%
 
ValueCountFrequency (%) 
13449.51< 0.1%
 
549.051< 0.1%
 
307.61< 0.1%
 
236.51< 0.1%
 
2351< 0.1%
 

O_proteins_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct252
Distinct (%)7.6%
Missing672
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean3.826612338
Minimum0
Maximum95
Zeros62
Zeros (%)1.6%
Memory size31.1 KiB
2020-10-08T12:54:19.137059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6999999881
Q11.3
median2
Q34.9
95-th percentile13.3
Maximum95
Range95
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation4.178203282
Coefficient of variation (CV)1.091880471
Kurtosis71.12425323
Mean3.826612338
Median Absolute Deviation (MAD)1.1
Skewness4.696697416
Sum12654.607
Variance17.45738267
MonotocityNot monotonic
2020-10-08T12:54:19.335739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.21734.3%
 
1.31604.0%
 
1.41513.8%
 
1.61473.7%
 
11453.6%
 
1.51373.4%
 
1.71052.6%
 
1.11032.6%
 
2631.6%
 
0621.6%
 
Other values (242)206151.8%
 
(Missing)67216.9%
 
ValueCountFrequency (%) 
0621.6%
 
0.1120.3%
 
0.10000000151< 0.1%
 
0.240.1%
 
0.2000000031< 0.1%
 
ValueCountFrequency (%) 
951< 0.1%
 
331< 0.1%
 
281< 0.1%
 
271< 0.1%
 
211< 0.1%
 

O_sodium_100g
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct495
Distinct (%)15.1%
Missing698
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean0.8864452601
Minimum0
Maximum40
Zeros39
Zeros (%)1.0%
Memory size31.1 KiB
2020-10-08T12:54:19.496370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.384
median0.52
Q30.84
95-th percentile2.4
Maximum40
Range40
Interquartile range (IQR)0.456

Descriptive statistics

Standard deviation1.837886342
Coefficient of variation (CV)2.073321867
Kurtosis239.2958372
Mean0.8864452601
Median Absolute Deviation (MAD)0.2
Skewness13.16695187
Sum2908.426898
Variance3.377826205
MonotocityNot monotonic
2020-10-08T12:54:19.656862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.42225.6%
 
0.481724.3%
 
0.61634.1%
 
0.441584.0%
 
0.521453.6%
 
0.721042.6%
 
0.68832.1%
 
0.36822.1%
 
0.56792.0%
 
0.32741.9%
 
Other values (485)199950.2%
 
(Missing)69817.5%
 
ValueCountFrequency (%) 
0391.0%
 
0.0002481< 0.1%
 
0.0041< 0.1%
 
0.00820.1%
 
0.01260.2%
 
ValueCountFrequency (%) 
4020.1%
 
39.921< 0.1%
 
34.41< 0.1%
 
23.1620.1%
 
18.241< 0.1%
 

O_salt_100g
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct564
Distinct (%)17.2%
Missing698
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean2.216110712
Minimum0
Maximum100
Zeros39
Zeros (%)1.0%
Memory size31.1 KiB
2020-10-08T12:54:19.822693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.425
Q10.96
median1.3
Q32.1
95-th percentile6
Maximum100
Range100
Interquartile range (IQR)1.14

Descriptive statistics

Standard deviation4.594715819
Coefficient of variation (CV)2.073324132
Kurtosis239.2958726
Mean2.216110712
Median Absolute Deviation (MAD)0.5
Skewness13.16695368
Sum7271.059246
Variance21.11141346
MonotocityNot monotonic
2020-10-08T12:54:19.979297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12165.4%
 
1.21684.2%
 
1.11563.9%
 
1.51533.8%
 
1.31353.4%
 
1.8962.4%
 
1.7771.9%
 
0.9771.9%
 
0.8731.8%
 
2.5721.8%
 
Other values (554)205851.7%
 
(Missing)69817.5%
 
ValueCountFrequency (%) 
0391.0%
 
0.000621< 0.1%
 
0.011< 0.1%
 
0.021< 0.1%
 
0.021< 0.1%
 
ValueCountFrequency (%) 
10020.1%
 
99.81< 0.1%
 
861< 0.1%
 
57.920.1%
 
45.61< 0.1%
 

O_nutrition_score_fr_100g
Real number (ℝ)

MISSING
ZEROS

Distinct38
Distinct (%)1.3%
Missing1163
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean9.287997159
Minimum-8
Maximum29
Zeros87
Zeros (%)2.2%
Memory size31.1 KiB
2020-10-08T12:54:20.125572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile-2
Q13
median10
Q315
95-th percentile21
Maximum29
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.55050336
Coefficient of variation (CV)0.8129312737
Kurtosis-0.9870312905
Mean9.287997159
Median Absolute Deviation (MAD)6
Skewness0.008248130178
Sum26155
Variance57.010101
MonotocityNot monotonic
2020-10-08T12:54:20.300339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
131754.4%
 
21403.5%
 
121363.4%
 
31343.4%
 
41303.3%
 
151233.1%
 
111223.1%
 
11112.8%
 
51102.8%
 
161092.7%
 
Other values (28)152638.4%
 
(Missing)116329.2%
 
ValueCountFrequency (%) 
-850.1%
 
-740.1%
 
-6110.3%
 
-5190.5%
 
-4370.9%
 
ValueCountFrequency (%) 
2920.1%
 
2820.1%
 
2780.2%
 
2650.1%
 
2560.2%
 

O_fiber_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct105
Distinct (%)6.7%
Missing2422
Missing (%)60.9%
Infinite0
Infinite (%)0.0%
Mean1.986347463
Minimum0
Maximum59
Zeros229
Zeros (%)5.8%
Memory size31.1 KiB
2020-10-08T12:54:20.461948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median1.5
Q32.5
95-th percentile5.5
Maximum59
Range59
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation2.50946711
Coefficient of variation (CV)1.263357573
Kurtosis175.3026934
Mean1.986347463
Median Absolute Deviation (MAD)1
Skewness9.014150818
Sum3092.743
Variance6.297425178
MonotocityNot monotonic
2020-10-08T12:54:20.615052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02295.8%
 
0.51102.8%
 
1711.8%
 
1.5691.7%
 
1.6591.5%
 
1.7531.3%
 
1.2491.2%
 
2.5481.2%
 
1.8441.1%
 
0.9431.1%
 
Other values (95)78219.7%
 
(Missing)242260.9%
 
ValueCountFrequency (%) 
02295.8%
 
0.061< 0.1%
 
0.140.1%
 
0.260.2%
 
0.3100.3%
 
ValueCountFrequency (%) 
591< 0.1%
 
18.31< 0.1%
 
161< 0.1%
 
1520.1%
 
1420.1%
 

O_nova_group_100g
Categorical

MISSING

Distinct4
Distinct (%)0.2%
Missing1659
Missing (%)41.7%
Memory size31.1 KiB
4
1377 
3
891 
1
 
42
2
 
10
ValueCountFrequency (%) 
4137734.6%
 
389122.4%
 
1421.1%
 
2100.3%
 
(Missing)165941.7%
 
2020-10-08T12:54:20.773171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:20.879602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:21.031083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

O_COUNT
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
1
3434 
(Missing)
545 
ValueCountFrequency (%) 
1343486.3%
 
(Missing)54513.7%
 
2020-10-08T12:54:21.116519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

O_date_modified
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing549
Missing (%)13.8%
Memory size31.1 KiB
20150316
2571 
20120622
608 
20190830
 
249
20130323
 
2
ValueCountFrequency (%) 
20150316257164.6%
 
2012062260815.3%
 
201908302496.3%
 
2013032320.1%
 
(Missing)54913.8%
 
2020-10-08T12:54:21.193591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:21.275229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:21.556703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.034179442
Min length3

O_BRANDS_UPPER
Categorical

HIGH CARDINALITY
MISSING

Distinct226
Distinct (%)6.6%
Missing545
Missing (%)13.7%
Memory size31.1 KiB
AUCHAN
293 
CARREFOUR
281 
U
273 
HEINZ
264 
PANZANI
 
171
Other values (221)
2152 
ValueCountFrequency (%) 
AUCHAN2937.4%
 
CARREFOUR2817.1%
 
U2736.9%
 
HEINZ2646.6%
 
PANZANI1714.3%
 
CASINO1443.6%
 
SACLA1373.4%
 
CORA1082.7%
 
BARILLA922.3%
 
ZAPETTI661.7%
 
Other values (216)160540.3%
 
(Missing)54513.7%
 
2020-10-08T12:54:21.927898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique72 ?
Unique (%)2.1%
2020-10-08T12:54:22.108762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length48
Median length6
Mean length7.127670269
Min length1

S_brands
Categorical

HIGH CARDINALITY
MISSING

Distinct258
Distinct (%)13.1%
Missing2004
Missing (%)50.4%
Memory size31.1 KiB
Heinz
256 
Panzani
164 
Sacla
134 
Barilla
 
89
Zapetti
 
63
Other values (253)
1269 
ValueCountFrequency (%) 
Heinz2566.4%
 
Panzani1644.1%
 
Sacla1343.4%
 
Barilla892.2%
 
Zapetti631.6%
 
Tramier591.5%
 
Lucien Georgelin471.2%
 
Mutti411.0%
 
Florelli381.0%
 
Marius Bernard320.8%
 
Other values (248)105226.4%
 
(Missing)200450.4%
 
2020-10-08T12:54:22.285430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique103 ?
Unique (%)5.2%
2020-10-08T12:54:22.465134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length48
Median length3
Mean length6.002010555
Min length2

S_BRAND
Categorical

HIGH CORRELATION
MISSING

Distinct36
Distinct (%)1.8%
Missing2004
Missing (%)50.4%
Memory size31.1 KiB
AUT_MQ
830 
HEINZ
258 
PANZANI
173 
SACLA
167 
BARILLA
90 
Other values (31)
457 
ValueCountFrequency (%) 
AUT_MQ83020.9%
 
HEINZ2586.5%
 
PANZANI1734.3%
 
SACLA1674.2%
 
BARILLA902.3%
 
ZAPETTI_BUITONI782.0%
 
LUCIEN_GEORGELIN481.2%
 
FLORELLI431.1%
 
MUTTI421.1%
 
JARDIN_BIO370.9%
 
Other values (26)2095.3%
 
(Missing)200450.4%
 
2020-10-08T12:54:22.621537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)0.2%
2020-10-08T12:54:22.770679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length3
Mean length5.004775069
Min length3

S_BRAND_TYPE
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.2%
Missing2004
Missing (%)50.4%
Memory size31.1 KiB
MARQUE
1129 
AUT_MQ
830 
MDD
 
16
ValueCountFrequency (%) 
MARQUE112928.4%
 
AUT_MQ83020.9%
 
MDD160.4%
 
(Missing)200450.4%
 
2020-10-08T12:54:22.913277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:22.996165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:23.103229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length4.477004272
Min length3

S_BRANDS_UPPER
Categorical

HIGH CARDINALITY
MISSING

Distinct217
Distinct (%)11.0%
Missing2004
Missing (%)50.4%
Memory size31.1 KiB
HEINZ
256 
PANZANI
171 
SACLA
137 
BARILLA
 
89
ZAPETTI
 
66
Other values (212)
1256 
ValueCountFrequency (%) 
HEINZ2566.4%
 
PANZANI1714.3%
 
SACLA1373.4%
 
BARILLA892.2%
 
ZAPETTI661.7%
 
TRAMIER591.5%
 
LUCIEN GEORGELIN481.2%
 
MUTTI421.1%
 
FLORELLI401.0%
 
MARIUS BERNARD370.9%
 
Other values (207)103025.9%
 
(Missing)200450.4%
 
2020-10-08T12:54:23.263400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique81 ?
Unique (%)4.1%
2020-10-08T12:54:23.432891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length48
Median length3
Mean length6.002010555
Min length2

Unnamed: 0.1
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
UNIFORM

Distinct1443
Distinct (%)100.0%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean721
Minimum0
Maximum1442
Zeros1
Zeros (%)< 0.1%
Memory size31.1 KiB
2020-10-08T12:54:23.586874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.1
Q1360.5
median721
Q31081.5
95-th percentile1369.9
Maximum1442
Range1442
Interquartile range (IQR)721

Descriptive statistics

Standard deviation416.7025318
Coefficient of variation (CV)0.5779508069
Kurtosis-1.2
Mean721
Median Absolute Deviation (MAD)361
Skewness0
Sum1040403
Variance173641
MonotocityNot monotonic
2020-10-08T12:54:23.742246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2421< 0.1%
 
2761< 0.1%
 
3161< 0.1%
 
3401< 0.1%
 
3961< 0.1%
 
4681< 0.1%
 
5081< 0.1%
 
5201< 0.1%
 
6001< 0.1%
 
7121< 0.1%
 
Other values (1433)143336.0%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
14421< 0.1%
 
14411< 0.1%
 
14401< 0.1%
 
14391< 0.1%
 
14381< 0.1%
 

df_index
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1443
Distinct (%)100.0%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean2319.543313
Minimum1542
Maximum3074
Zeros0
Zeros (%)0.0%
Memory size31.1 KiB
2020-10-08T12:54:24.032293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1542
5-th percentile1625.1
Q11958.5
median2319
Q32695.5
95-th percentile2992.9
Maximum3074
Range1532
Interquartile range (IQR)737

Descriptive statistics

Standard deviation434.1421276
Coefficient of variation (CV)0.1871670709
Kurtosis-1.15603261
Mean2319.543313
Median Absolute Deviation (MAD)368
Skewness-0.03036599797
Sum3347101
Variance188479.387
MonotocityNot monotonic
2020-10-08T12:54:24.201598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
23761< 0.1%
 
27851< 0.1%
 
29071< 0.1%
 
21251< 0.1%
 
24391< 0.1%
 
23931< 0.1%
 
23231< 0.1%
 
18371< 0.1%
 
26411< 0.1%
 
19511< 0.1%
 
Other values (1433)143336.0%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
15421< 0.1%
 
15431< 0.1%
 
15441< 0.1%
 
15451< 0.1%
 
15471< 0.1%
 
ValueCountFrequency (%) 
30741< 0.1%
 
30721< 0.1%
 
30701< 0.1%
 
30691< 0.1%
 
30681< 0.1%
 

N_EAN13
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1426
Distinct (%)98.8%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean5.066489031e+12
Minimum0
Maximum9.556041612e+12
Zeros11
Zeros (%)0.3%
Memory size31.1 KiB
2020-10-08T12:54:24.372377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.021690019e+12
Q13.142952721e+12
median3.760020501e+12
Q38.001970095e+12
95-th percentile8.076809574e+12
Maximum9.556041612e+12
Range9.556041612e+12
Interquartile range (IQR)4.859017374e+12

Descriptive statistics

Standard deviation2.42109541e+12
Coefficient of variation (CV)0.477864532
Kurtosis-1.394171812
Mean5.066489031e+12
Median Absolute Deviation (MAD)7.26310414e+11
Skewness0.2741464352
Sum7.310943672e+15
Variance5.861702982e+24
MonotocityNot monotonic
2020-10-08T12:54:24.538225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0110.3%
 
3.038354198e+1220.1%
 
3.038359005e+1220.1%
 
3.183811011e+1220.1%
 
3.038354197e+1220.1%
 
3.038359008e+1220.1%
 
3.038359008e+1220.1%
 
3.038359005e+1220.1%
 
3.069533173e+121< 0.1%
 
3.069538103e+121< 0.1%
 
Other values (1416)141635.6%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
0110.3%
 
401987981< 0.1%
 
407889751< 0.1%
 
407889821< 0.1%
 
800243471< 0.1%
 
ValueCountFrequency (%) 
9.556041612e+121< 0.1%
 
9.556041131e+121< 0.1%
 
9.325763e+121< 0.1%
 
9.31043233e+121< 0.1%
 
9.002859071e+121< 0.1%
 

N_MARQUE
Categorical

HIGH CORRELATION
MISSING

Distinct31
Distinct (%)2.1%
Missing2536
Missing (%)63.7%
Memory size31.1 KiB
AUT_MQ
667 
PANZANI
179 
SACLA
98 
ZAPETTI_BUITONI
95 
BARILLA
79 
Other values (26)
325 
ValueCountFrequency (%) 
AUT_MQ66716.8%
 
PANZANI1794.5%
 
SACLA982.5%
 
ZAPETTI_BUITONI952.4%
 
BARILLA792.0%
 
HEINZ611.5%
 
FLORELLI381.0%
 
JARDIN_BIO330.8%
 
LUCIEN_GEORGELIN220.6%
 
LOUIS_MARTIN210.5%
 
Other values (21)1503.8%
 
(Missing)253663.7%
 
2020-10-08T12:54:24.916580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.2%
2020-10-08T12:54:25.067804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length3
Mean length4.590349334
Min length3

N_SAME_PRODUCT
Categorical

HIGH CARDINALITY
MISSING

Distinct782
Distinct (%)54.2%
Missing2536
Missing (%)63.7%
Memory size31.1 KiB
AUT_MQ | PESTO | PESTO_VERD | POT | NON_BIO
 
47
AUT_MQ | LEGUMES | BASILIC | POT | NON_BIO
 
45
AUT_MQ | BOLO_CLASSIQUE | BOLOGNAISE | POT | NON_BIO
 
32
AUT_MQ | LEGUMES | ARRABIATA | POT | NON_BIO
 
30
PANZANI | BOLO_CLASSIQUE | BOLOGNAISE | POT | NON_BIO
 
26
Other values (777)
1263 
ValueCountFrequency (%) 
AUT_MQ | PESTO | PESTO_VERD | POT | NON_BIO471.2%
 
AUT_MQ | LEGUMES | BASILIC | POT | NON_BIO451.1%
 
AUT_MQ | BOLO_CLASSIQUE | BOLOGNAISE | POT | NON_BIO320.8%
 
AUT_MQ | LEGUMES | ARRABIATA | POT | NON_BIO300.8%
 
PANZANI | BOLO_CLASSIQUE | BOLOGNAISE | POT | NON_BIO260.7%
 
AUT_MQ | PESTO | PESTO_RGE | POT | NON_BIO200.5%
 
AUT_MQ | LEGUMES | NATURE | POT | NON_BIO190.5%
 
PANZANI | LEGUMES | PROVENCALE | POT | NON_BIO170.4%
 
AUT_MQ | BIO | BASILIC | POT | BIO120.3%
 
AUT_MQ | LEGUMES | NAPOLITAIN | POT | NON_BIO120.3%
 
Other values (772)118329.7%
 
(Missing)253663.7%
 
2020-10-08T12:54:25.599150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique608 ?
Unique (%)42.1%
2020-10-08T12:54:25.776787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length81
Median length3
Mean length19.55013823
Min length3

N_GAMME
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)1.0%
Missing2547
Missing (%)64.0%
Memory size31.1 KiB
LEGUMES
667 
PESTO
168 
BIO
163 
BOLO_CLASSIQUE
152 
SCE_CLAS
146 
Other values (10)
136 
ValueCountFrequency (%) 
LEGUMES66716.8%
 
PESTO1684.2%
 
BIO1634.1%
 
BOLO_CLASSIQUE1523.8%
 
SCE_CLAS1463.7%
 
BOLO_ORIGINALE350.9%
 
AUT_VIANDE230.6%
 
CREME200.5%
 
AUTRE_GAMME200.5%
 
SCE_POISSON120.3%
 
Other values (5)260.7%
 
(Missing)254764.0%
 
2020-10-08T12:54:25.941664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-08T12:54:26.072107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length3
Mean length4.649660719
Min length3

N_RECETTES
Categorical

HIGH CARDINALITY
MISSING

Distinct477
Distinct (%)33.3%
Missing2547
Missing (%)64.0%
Memory size31.1 KiB
BASILIC
114 
BOLOGNAISE
111 
PESTO_VERD
 
81
ARRABIATA
 
71
PROVENCALE
 
60
Other values (472)
995 
ValueCountFrequency (%) 
BASILIC1142.9%
 
BOLOGNAISE1112.8%
 
PESTO_VERD812.0%
 
ARRABIATA711.8%
 
PROVENCALE601.5%
 
PESTO_RGE411.0%
 
NATURE340.9%
 
NAPOLITAIN320.8%
 
TOMATE_CUI300.8%
 
OLIVE290.7%
 
Other values (467)82920.8%
 
(Missing)254764.0%
 
2020-10-08T12:54:26.227844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique350 ?
Unique (%)24.4%
2020-10-08T12:54:26.393926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length3
Mean length6.205579291
Min length1

N_ORGANIC
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.2%
Missing2536
Missing (%)63.7%
Memory size31.1 KiB
NON_BIO
1250 
BIO
182 
separator
 
11
ValueCountFrequency (%) 
NON_BIO125031.4%
 
BIO1824.6%
 
separator110.3%
 
(Missing)253663.7%
 
2020-10-08T12:54:26.536831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:26.647201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:26.781954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length3
Mean length4.273184217
Min length3

N_FORMAT
Categorical

MISSING

Distinct6
Distinct (%)0.4%
Missing2547
Missing (%)64.0%
Memory size31.1 KiB
PF
894 
MF
455 
GF
 
46
FI
 
30
TGF
 
6
ValueCountFrequency (%) 
PF89422.5%
 
MF45511.4%
 
GF461.2%
 
FI300.8%
 
TGF60.2%
 
AUT_FORM1< 0.1%
 
(Missing)254764.0%
 
2020-10-08T12:54:26.994811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-08T12:54:28.377123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:28.499251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length2.643126414
Min length2

N_WEIGHT_num
Real number (ℝ≥0)

MISSING

Distinct85
Distinct (%)5.9%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean281.002772
Minimum0
Maximum4200
Zeros12
Zeros (%)0.3%
Memory size31.1 KiB
2020-10-08T12:54:28.640802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile120
Q1190
median240
Q3350
95-th percentile500
Maximum4200
Range4200
Interquartile range (IQR)160

Descriptive statistics

Standard deviation191.4725331
Coefficient of variation (CV)0.6813901931
Kurtosis166.1863292
Mean281.002772
Median Absolute Deviation (MAD)60
Skewness9.824229265
Sum405487
Variance36661.73092
MonotocityNot monotonic
2020-10-08T12:54:28.793536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1902847.1%
 
4001393.5%
 
1801193.0%
 
2001112.8%
 
350832.1%
 
250561.4%
 
300421.1%
 
290360.9%
 
210350.9%
 
280320.8%
 
Other values (75)50612.7%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
0120.3%
 
401< 0.1%
 
5070.2%
 
8030.1%
 
90100.3%
 
ValueCountFrequency (%) 
42001< 0.1%
 
280020.1%
 
20001< 0.1%
 
10001< 0.1%
 
8001< 0.1%
 

N_EMBALLAGE
Categorical

MISSING

Distinct10
Distinct (%)0.7%
Missing2536
Missing (%)63.7%
Memory size31.1 KiB
POT
1276 
BTE_FER
 
66
DOY_PAC
 
29
BRIC
 
25
TUB
 
23
Other values (5)
 
24
ValueCountFrequency (%) 
POT127632.1%
 
BTE_FER661.7%
 
DOY_PAC290.7%
 
BRIC250.6%
 
TUB230.6%
 
FLA_PLAS90.2%
 
DOSE80.2%
 
AUT_EMB40.1%
 
COUPELLE20.1%
 
SAC1< 0.1%
 
(Missing)253663.7%
 
2020-10-08T12:54:28.941163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-08T12:54:29.033068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:29.187705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length3.121638603
Min length3

N_COMPTE
Categorical

MISSING

Distinct7
Distinct (%)0.5%
Missing2547
Missing (%)64.0%
Memory size31.1 KiB
1CT
1250 
2CT
 
101
3CT
 
47
12CT
 
13
6CT
 
10
Other values (2)
 
11
ValueCountFrequency (%) 
1CT125031.4%
 
2CT1012.5%
 
3CT471.2%
 
12CT130.3%
 
6CT100.3%
 
4CT60.2%
 
24CT50.1%
 
(Missing)254764.0%
 
2020-10-08T12:54:29.317521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:29.407033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:29.535960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.00452375
Min length3

N_STD/PROMO
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing2547
Missing (%)64.0%
Memory size31.1 KiB
STD
1302 
PROMO
 
130
ValueCountFrequency (%) 
STD130232.7%
 
PROMO1303.3%
 
(Missing)254764.0%
 
2020-10-08T12:54:29.657400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:29.736397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:29.840023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.065343051
Min length3

N_Ventes_Valeur_2019
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1143
Distinct (%)79.2%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean238260.1597
Minimum0
Maximum94370200.5
Zeros271
Zeros (%)6.8%
Memory size31.1 KiB
2020-10-08T12:54:30.005278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.4
median4451.9
Q335117.95
95-th percentile780618.28
Maximum94370200.5
Range94370200.5
Interquartile range (IQR)35096.55

Descriptive statistics

Standard deviation2579302.266
Coefficient of variation (CV)10.82557096
Kurtosis1232.732984
Mean238260.1597
Median Absolute Deviation (MAD)4451.9
Skewness33.90402418
Sum343809410.4
Variance6.65280018e+12
MonotocityNot monotonic
2020-10-08T12:54:30.198187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02716.8%
 
3.840.1%
 
1.440.1%
 
2.540.1%
 
7.530.1%
 
4.630.1%
 
1.920.1%
 
48.220.1%
 
2.220.1%
 
4.120.1%
 
Other values (1133)114628.8%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
02716.8%
 
0.81< 0.1%
 
1.11< 0.1%
 
1.31< 0.1%
 
1.440.1%
 
ValueCountFrequency (%) 
94370200.51< 0.1%
 
10428060.81< 0.1%
 
8849851.81< 0.1%
 
8294370.11< 0.1%
 
5817400.11< 0.1%
 

N_Ventes_Valeur_2018
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1117
Distinct (%)77.4%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean229343.4008
Minimum0
Maximum88010805
Zeros297
Zeros (%)7.5%
Memory size31.1 KiB
2020-10-08T12:54:30.381756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.3
median2857
Q334837.05
95-th percentile745454.12
Maximum88010805
Range88010805
Interquartile range (IQR)34825.75

Descriptive statistics

Standard deviation2415907.043
Coefficient of variation (CV)10.53401595
Kurtosis1211.303545
Mean229343.4008
Median Absolute Deviation (MAD)2857
Skewness33.4951491
Sum330942527.4
Variance5.836606842e+12
MonotocityNot monotonic
2020-10-08T12:54:30.537017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02977.5%
 
2.350.1%
 
1.540.1%
 
3.330.1%
 
530.1%
 
2.830.1%
 
118.630.1%
 
34.820.1%
 
12.120.1%
 
4.320.1%
 
Other values (1107)111928.1%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
02977.5%
 
0.71< 0.1%
 
0.81< 0.1%
 
1.21< 0.1%
 
1.31< 0.1%
 
ValueCountFrequency (%) 
880108051< 0.1%
 
9922042.21< 0.1%
 
9563478.31< 0.1%
 
7921644.11< 0.1%
 
6925901.31< 0.1%
 

N_ITEM
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1432
Distinct (%)100.0%
Missing2547
Missing (%)64.0%
Memory size31.1 KiB
3700656403190
 
1
3660603080808
 
1
8005360001164
 
1
9556041612005
 
1
3038359004209
 
1
Other values (1427)
1427 
ValueCountFrequency (%) 
37006564031901< 0.1%
 
36606030808081< 0.1%
 
80053600011641< 0.1%
 
95560416120051< 0.1%
 
30383590042091< 0.1%
 
80029605022061< 0.1%
 
80017700622381< 0.1%
 
80013101005121< 0.1%
 
88536620405161< 0.1%
 
30383590041311< 0.1%
 
Other values (1422)142235.7%
 
(Missing)254764.0%
 
2020-10-08T12:54:30.799819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1432 ?
Unique (%)100.0%
2020-10-08T12:54:31.357959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length3
Mean length6.610203569
Min length3

N_NIELSEN_DESCRIPTION
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1212
Distinct (%)84.0%
Missing2536
Missing (%)63.7%
Memory size31.1 KiB
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_PESTO_PF_PESTO_VERD_1CT_180G_*_NON_BIO
 
13
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_PESTO_PF_PESTO_RGE_1CT_180G_*_NON_BIO
 
8
FECULENTS_POT_AUT_FAB_AUT_MQ_BOLO_CLASSIQUE_MF_BOLOGNAISE_1CT_350G_*_NON_BIO
 
7
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_LEGUMES_PF_ARRABIATA_1CT_190G_*_NON_BIO
 
7
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_PESTO_PF_PESTO_VERD_1CT_190G_*_NON_BIO
 
7
Other values (1207)
1401 
ValueCountFrequency (%) 
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_PESTO_PF_PESTO_VERD_1CT_180G_*_NON_BIO130.3%
 
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_PESTO_PF_PESTO_RGE_1CT_180G_*_NON_BIO80.2%
 
FECULENTS_POT_AUT_FAB_AUT_MQ_BOLO_CLASSIQUE_MF_BOLOGNAISE_1CT_350G_*_NON_BIO70.2%
 
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_LEGUMES_PF_ARRABIATA_1CT_190G_*_NON_BIO70.2%
 
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_PESTO_PF_PESTO_VERD_1CT_190G_*_NON_BIO70.2%
 
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_PESTO_PF_PESTO_RGE_1CT_190G_*_NON_BIO70.2%
 
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_LEGUMES_MF_BASILIC_1CT_350G_*_NON_BIO60.2%
 
FECULENTS_POT_AUT_FAB_AUT_MQ_BOLO_CLASSIQUE_PF_BOLOGNAISE_1CT_190G_*_NON_BIO60.2%
 
FECULENTS_POT_PANZANI_SA_PANZANI_BOLO_CLASSIQUE_PF_BOLOGNAISE_2CT_210G_*_NON_BIO50.1%
 
SAUCES_POUR_FECULENTS_POT_AUT_FAB_AUT_MQ_LEGUMES_PF_ARRABIATA_1CT_180G_*_NON_BIO50.1%
 
Other values (1202)137234.5%
 
(Missing)253663.7%
 
2020-10-08T12:54:31.518699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1077 ?
Unique (%)74.6%
2020-10-08T12:54:31.686444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length80
Median length3
Mean length29.27368686
Min length3

N_INITIAL_INDEX
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1443
Distinct (%)100.0%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean2319.543313
Minimum1542
Maximum3074
Zeros0
Zeros (%)0.0%
Memory size31.1 KiB
2020-10-08T12:54:31.842607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1542
5-th percentile1625.1
Q11958.5
median2319
Q32695.5
95-th percentile2992.9
Maximum3074
Range1532
Interquartile range (IQR)737

Descriptive statistics

Standard deviation434.1421276
Coefficient of variation (CV)0.1871670709
Kurtosis-1.15603261
Mean2319.543313
Median Absolute Deviation (MAD)368
Skewness-0.03036599797
Sum3347101
Variance188479.387
MonotocityNot monotonic
2020-10-08T12:54:31.999372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
23761< 0.1%
 
27851< 0.1%
 
29071< 0.1%
 
21251< 0.1%
 
24391< 0.1%
 
23931< 0.1%
 
23231< 0.1%
 
18371< 0.1%
 
26411< 0.1%
 
19511< 0.1%
 
Other values (1433)143336.0%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
15421< 0.1%
 
15431< 0.1%
 
15441< 0.1%
 
15451< 0.1%
 
15471< 0.1%
 
ValueCountFrequency (%) 
30741< 0.1%
 
30721< 0.1%
 
30701< 0.1%
 
30691< 0.1%
 
30681< 0.1%
 

N_CATEGORY
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing2536
Missing (%)63.7%
Memory size31.1 KiB
SAUCES_POUR_FECULENTS
1294 
AUT_SCE
149 
ValueCountFrequency (%) 
SAUCES_POUR_FECULENTS129432.5%
 
AUT_SCE1493.7%
 
(Missing)253663.7%
 
2020-10-08T12:54:32.149800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:32.225406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:32.315546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length3
Mean length9.003518472
Min length3

N_COUNT
Boolean

MISSING

Distinct1
Distinct (%)0.1%
Missing2536
Missing (%)63.7%
Memory size31.1 KiB
1
1443 
(Missing)
2536 
ValueCountFrequency (%) 
1144336.3%
 
(Missing)253663.7%
 
2020-10-08T12:54:32.402159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

I_ITEM
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1272
Distinct (%)100.0%
Missing2707
Missing (%)68.0%
Memory size31.1 KiB
5413171273619
 
1
8005360001164
 
1
9556041612005
 
1
3038359004209
 
1
8002960502206
 
1
Other values (1267)
1267 
ValueCountFrequency (%) 
54131712736191< 0.1%
 
80053600011641< 0.1%
 
95560416120051< 0.1%
 
30383590042091< 0.1%
 
80029605022061< 0.1%
 
80017700622381< 0.1%
 
88536620405161< 0.1%
 
30383590041311< 0.1%
 
80325514455371< 0.1%
 
80590707415751< 0.1%
 
Other values (1262)126231.7%
 
(Missing)270768.0%
 
2020-10-08T12:54:32.509881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1272 ?
Unique (%)100.0%
2020-10-08T12:54:32.667382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length3
Mean length6.208092486
Min length3

I_CATEGORY
Categorical

MISSING

Distinct15
Distinct (%)1.3%
Missing2867
Missing (%)72.1%
Memory size31.1 KiB
BOLOGNAISE
208 
LEGUMES
132 
PESTO_VERT
114 
BASILIC
102 
POMODORO
82 
Other values (10)
474 
ValueCountFrequency (%) 
BOLOGNAISE2085.2%
 
LEGUMES1323.3%
 
PESTO_VERT1142.9%
 
BASILIC1022.6%
 
POMODORO822.1%
 
ARRABBIATA691.7%
 
AUTRE681.7%
 
FROMAGE661.7%
 
PESTO_ROUGE651.6%
 
OLIVES591.5%
 
Other values (5)1473.7%
 
(Missing)286772.1%
 
2020-10-08T12:54:32.805117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T12:54:32.950767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length4.550892184
Min length3

I_Ventes_Volume_2018
Real number (ℝ≥0)

MISSING
ZEROS

Distinct904
Distinct (%)62.6%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean20664.64394
Minimum0
Maximum1619277.8
Zeros467
Zeros (%)11.7%
Memory size31.1 KiB
2020-10-08T12:54:33.122549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median73.9
Q31644
95-th percentile83142.82
Maximum1619277.8
Range1619277.8
Interquartile range (IQR)1644

Descriptive statistics

Standard deviation100036.5607
Coefficient of variation (CV)4.840952546
Kurtosis89.209099
Mean20664.64394
Median Absolute Deviation (MAD)73.9
Skewness8.437221237
Sum29819081.2
Variance1.000731347e+10
MonotocityNot monotonic
2020-10-08T12:54:33.286412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
046711.7%
 
0.890.2%
 
0.380.2%
 
0.470.2%
 
0.660.2%
 
0.250.1%
 
2.530.1%
 
7.230.1%
 
1.630.1%
 
8.130.1%
 
Other values (894)92923.3%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
046711.7%
 
0.250.1%
 
0.380.2%
 
0.470.2%
 
0.530.1%
 
ValueCountFrequency (%) 
1619277.81< 0.1%
 
1097964.61< 0.1%
 
1076764.71< 0.1%
 
1039552.41< 0.1%
 
921213.11< 0.1%
 

I_Ventes_Volume_2019
Real number (ℝ≥0)

MISSING
ZEROS

Distinct907
Distinct (%)62.9%
Missing2536
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean19924.79466
Minimum0
Maximum1198246.6
Zeros447
Zeros (%)11.2%
Memory size31.1 KiB
2020-10-08T12:54:33.492841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median93.1
Q31796.9
95-th percentile77958.6
Maximum1198246.6
Range1198246.6
Interquartile range (IQR)1796.9

Descriptive statistics

Standard deviation98509.00428
Coefficient of variation (CV)4.944041128
Kurtosis67.93224872
Mean19924.79466
Median Absolute Deviation (MAD)93.1
Skewness7.678490963
Sum28751478.7
Variance9704023924
MonotocityNot monotonic
2020-10-08T12:54:33.693266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
044711.2%
 
0.4150.4%
 
0.280.2%
 
0.670.2%
 
0.870.2%
 
240.1%
 
1.440.1%
 
10.230.1%
 
2.830.1%
 
1.230.1%
 
Other values (897)94223.7%
 
(Missing)253663.7%
 
ValueCountFrequency (%) 
044711.2%
 
0.280.2%
 
0.330.1%
 
0.4150.4%
 
0.670.2%
 
ValueCountFrequency (%) 
1198246.61< 0.1%
 
1166537.51< 0.1%
 
1160097.41< 0.1%
 
967241.41< 0.1%
 
907869.91< 0.1%
 

Interactions

2020-10-08T12:51:49.144570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:49.299310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:49.413984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:49.535657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:49.648336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:49.767410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:49.876195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:49.992145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:50.103773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:50.223059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:50.332515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:50.452994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:50.564491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:50.679882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:50.799442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:50.918819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:51.144145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:51.261095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:51.376453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:51.487917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:51.598331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:51.717995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:51.837178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:51.952318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.065505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.180277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.295878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.415093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.525617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.643346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.759598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.874148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:52.996979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:53.110087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:53.228415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:53.337423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:53.451755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:53.560094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:53.679597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:53.791026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:53.911255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:54.027526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:54.144867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:54.265612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:54.386259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:54.500993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:54.744831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:54.860720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:54.971904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:55.088980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:55.208651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:55.328855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:55.444754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:55.554958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:55.670247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:55.785663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:55.905505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:56.016544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:56.135625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:56.260642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:56.386237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:56.517159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:56.639654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:56.767261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:56.885450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:57.009570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:57.126976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:57.255546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:57.375563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:57.505097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:57.624816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:57.748487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:57.882784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:58.012211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:58.136538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:58.262097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:58.386956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:58.505888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:58.626083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:58.755425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:58.900106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:59.039902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:51:59.346371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-08T12:53:24.733818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:24.846963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:24.967327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:25.078139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:25.226977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:25.333390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:25.446663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:25.553077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:25.670801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:25.778744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:25.904490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.013487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.127408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.245243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.378717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.491200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.604586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.717444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.824611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:26.979847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:27.388774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:27.527460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:28.021465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:28.288046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:28.401198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:28.513951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:28.632579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:28.742441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:28.875391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:28.991836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:29.107210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:29.275302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:29.391827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:29.509721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:29.616109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:29.805060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:30.852876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:31.017961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:31.187226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:31.329316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:31.530971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:31.649497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:31.794236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:31.911552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:32.023402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:32.136321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:32.249208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:32.361425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:32.471584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:32.796578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:32.915678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:33.055990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:33.248182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:33.361043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:33.473162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:33.590969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:33.699524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:33.819385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:33.940867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:34.109050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:34.236775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:34.354432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:34.477650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:34.590443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:34.710885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:34.823716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:34.949105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:35.064165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:35.211075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:35.326490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:35.447820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:35.571640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:35.695864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:35.814360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:35.935571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:36.056009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:36.171050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:36.286872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:36.440386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:36.903784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:37.068336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:37.182398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:37.302606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:37.423298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:37.547669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:37.663521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:37.860718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:37.969711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:38.076080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:38.189870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:38.327596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:38.453460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:38.701327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:38.875471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:38.975361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:39.085927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:39.186873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:39.299085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:39.401625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:39.698781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:39.809931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:39.921883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.028292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.135785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.242110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.342665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.445376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.555840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.675334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.782547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.884410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:40.992721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:41.099063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:41.213436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:41.324675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:41.434680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:41.553980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:41.672583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:41.797800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:41.938191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.060036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.170786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.288414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.400128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.523046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.636081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.759798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.873694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:42.991377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:43.114868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:43.236397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:43.354292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:43.486156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:43.609380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:43.721738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:43.835432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:43.956317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:44.078621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:44.197393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:44.308906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:44.428200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:44.545715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:44.737833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:44.991457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-08T12:54:33.893955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-08T12:54:34.374225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-08T12:54:34.854137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-08T12:54:35.363995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-08T12:54:35.924933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-08T12:53:46.040440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:52.553435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:53:55.932259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T12:54:02.153388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Unnamed: 0O_EAN13O_product_name_frO_product_nameO_brandsO_brands_tags_strO_brands_tagsO_serving_sizeO_serving_quantityO_countries_hierarchyO_cities_tagsO_manufacturing_placesO_purchase_placesO_countries_tagsO_categoriesO_category_propertiesO_pnns_groups_1O_pnns_groups_2O_compared_to_categoryO_interface_version_modifiedO_ingredients_nO_ingredients_textO_ingredientsO_salt_contentO_max_salt_contentO_min_salt_contentO_oil_contentO_max_oil_contentO_min_oil_contentO_oil_typeO_nutrimentsO_energy_100gO_energy_kcal_100gO_saturated_fat_100gO_fat_100gO_carbohydrates_100gO_sugars_100gO_fruits_vegetables_nuts_estimate_from_ingredients_100gO_proteins_100gO_sodium_100gO_salt_100gO_nutrition_score_fr_100gO_fiber_100gO_nova_group_100gO_COUNTO_date_modifiedO_BRANDS_UPPERS_brandsS_BRANDS_BRAND_TYPES_BRANDS_UPPERUnnamed: 0.1df_indexN_EAN13N_MARQUEN_SAME_PRODUCTN_GAMMEN_RECETTESN_ORGANICN_FORMATN_WEIGHT_numN_EMBALLAGEN_COMPTEN_STD/PROMON_Ventes_Valeur_2019N_Ventes_Valeur_2018N_ITEMN_NIELSEN_DESCRIPTIONN_INITIAL_INDEXN_CATEGORYN_COUNTI_ITEMI_CATEGORYI_Ventes_Volume_2018I_Ventes_Volume_2019
008.715722e+07Tomato Ketchup Heinz Ouverture En BasTomato Ketchup Heinz Ouverture En BasHeinz['heinz']['heinz']NaNNaN['en:france']NaNNaNNaN['en:france']Epicerie, Sauces, Sauces tomate, Ketchup{'ciqual_food_name:en': 'Ketchup', 'ciqual_food_name:fr': 'Ketchup'}Fat and saucesDressings and saucesen:ketchup20150316.jqm27.0Tomates (148 g pour 100 g de Tomato Ketchup), vinaigre, sucre, sel, extraits d'épices et d'herbes (contiennent du céleri) épice.['Tomates', 'vinaigre', 'sucre', 'sel', "extraits d'épices et d'herbes", 'épice', 'contiennent du céleri'][{'rank': 4, 'vegetarian': 'yes', 'text': 'sel', 'vegan': 'yes', 'id': 'en:salt'}]0.0000000.0[]0.00.0NaN{'fat_value': 0.1, 'nova-group_100g': 3, 'sugars_value': 22.8, 'proteins_100g': 1.2, 'sodium_100g': 0.72, 'energy-kcal_unit': 'kcal', 'sodium': 0.72, 'saturated-fat': 0.1, 'fat_100g': 0.1, 'energy-kcal_100g': 102, 'energy-kcal': 102, 'nutrition-score-fr_100g': 13, 'nova-group_serving': 3, 'carbohydrates_value': 23.2, 'energy_unit': 'kcal', 'carbohydrates_unit': '', 'salt_100g': 1.8, 'sugars_unit': '', 'saturated-fat_100g': 0.1, 'saturated-fat_unit': '', 'sugars_100g': 22.8, 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'fat_unit': '', 'sugars': 22.8, 'saturated-fat_value': 0.1, 'nova-group': 3, 'sodium_value': 0.72, 'energy_value': 102, 'fat': 0.1, 'carbohydrates': 23.2, 'salt': 1.8, 'energy-kcal_value': 102, 'carbohydrates_100g': 23.2, 'salt_value': 1.8, 'nutrition-score-fr_serving': 13, 'nutrition-score-fr': 13, 'sodium_unit': 'g', 'energy': 427, 'salt_unit': '', 'proteins_unit': '', 'proteins_value': 1.2, 'proteins': 1.2, 'energy_100g': 427}427.0102.00.100.123.2022.800.0000001.200.72001.800013.0NaN3.01.020150316.0HEINZHeinzHEINZMARQUEHEINZNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
115.015785e+07Heinz Squeeze Sauce Salade CreamHeinz Squeeze Sauce Salade CreamHeinz['heinz']['heinz']15 g15.0['en:france'][]NaNFrance['en:france']Epicerie, Sauces{}Fat and saucesDressings and saucesen:sauces20150316.jqm210.0Vinaigre d'alcool, eau, huile de pépins de raisin 22%, sucre, farine de maïs, _moutarde_, jaune d’_œuf_ pasteurisé 3%, sel, colorant : riboflavine.["Vinaigre d'alcool", 'eau', 'huile de pépins de raisin', 'sucre', 'farine de maïs', '_moutarde_', "jaune d'_œuf_ pasteurisé", 'sel', 'colorant', 'riboflavine'][{'id': 'en:salt', 'vegetarian': 'yes', 'rank': 8, 'text': 'sel', 'vegan': 'yes', 'percent_min': 0, 'percent_max': '3'}]3.0000000.0[]0.00.0NaN{'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'sugars_100g': 17, 'sugars': 17, 'fat_unit': '', 'saturated-fat_value': 1.8, 'carbon-footprint-from-known-ingredients_product': 276, 'sugars_serving': 2.55, 'sugars_unit': '', 'saturated-fat_unit': '', 'saturated-fat_100g': 1.8, 'salt_serving': 0.255, 'carbohydrates_value': 18.5, 'fat_serving': 3.57, 'nova-group_serving': 4, 'carbohydrates_unit': '', 'energy_unit': 'kcal', 'salt_100g': 1.7, 'carbon-footprint-from-known-ingredients_serving': 9.75, 'carbohydrates_serving': 2.77, 'nova-group_100g': 4, 'fat_value': 23.8, 'sodium_100g': 0.6799999999999999, 'sugars_value': 17, 'proteins_100g': 1.3, 'saturated-fat': 1.8, 'energy-kcal_unit': 'kcal', 'sodium': 0.6799999999999999, 'nutrition-score-fr_100g': 14, 'energy-kcal': 293, 'energy-kcal_100g': 293, 'fat_100g': 23.8, 'energy': 1226, 'energy_serving': 184, 'salt_unit': '', 'proteins_unit': '', 'proteins_value': 1.3, 'energy_100g': 1226, 'proteins': 1.3, 'nutrition-score-fr': 14, 'sodium_unit': 'g', 'sodium_serving': 0.10200000000000001, 'energy-kcal_value': 293, 'carbohydrates_100g': 18.5, 'salt': 1.7, 'carbon-footprint-from-known-ingredients_100g': 65, 'salt_value': 1.7, 'energy-kcal_serving': 44, 'nutrition-score-fr_serving': 14, 'saturated-fat_serving': 0.27, 'nova-group': 4, 'sodium_value': 0.6799999999999999, 'fat': 23.8, 'carbohydrates': 18.5, 'proteins_serving': 0.195, 'energy_value': 293}1226.0293.01.8023.818.5017.000.0000001.300.68001.700014.0NaN4.01.020150316.0HEINZHeinzHEINZMARQUEHEINZNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
228.715764e+07Tomato KetchupTomato KetchupHeinz['heinz']['heinz']NaNNaN['en:france'][]NaNFrance['en:france']Epicerie, Sauces, Sauces tomate, Ketchup{'ciqual_food_name:fr': 'Ketchup', 'ciqual_food_name:en': 'Ketchup'}Fat and saucesDressings and saucesen:ketchup201206227.0Tomates (148 g pour 100 g de Tomato Ketchup), vinaigre, sucre, sel, extraits d'épices et d'herbes (contiennent du _céleri_), épice.['Tomates', 'vinaigre', 'sucre', 'sel', "extraits d'épices et d'herbes", 'épice', 'contiennent du _céleri_'][{'id': 'en:salt', 'rank': 4, 'text': 'sel', 'vegetarian': 'yes', 'vegan': 'yes'}]0.0000000.0[]0.00.0NaN{'sugars_unit': 'g', 'saturated-fat_unit': 'g', 'saturated-fat_100g': 0.1, 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'sugars_100g': 22.8, 'sugars': 22.8, 'fat_unit': 'g', 'saturated-fat_value': 0.1, 'nova-group_100g': 3, 'fat_value': 0.1, 'sodium_100g': 0.72, 'sugars_value': 22.8, 'proteins_100g': 1.2, 'saturated-fat': 0.1, 'sodium': 0.72, 'energy-kcal_unit': 'kcal', 'energy-kcal': 102, 'nutrition-score-fr_100g': 13, 'energy-kcal_100g': 102, 'fat_100g': 0.1, 'carbohydrates_value': 23.2, 'nova-group_serving': 3, 'carbohydrates_unit': 'g', 'energy_unit': 'kcal', 'salt_100g': 1.7999999999999998, 'nutrition-score-fr': 13, 'sodium_unit': 'g', 'energy': 427, 'salt_unit': 'g', 'saturated-fat_modifier': '<', 'proteins_unit': 'g', 'proteins_value': 1.2, 'proteins': 1.2, 'energy_100g': 427, 'nova-group': 3, 'sodium_value': 0.72, 'fat': 0.1, 'carbohydrates': 23.2, 'energy_value': 102, 'carbohydrates_100g': 23.2, 'energy-kcal_value': 102, 'salt': 1.7999999999999998, 'salt_value': 1.8, 'nutrition-score-fr_serving': 13}427.0102.00.100.123.2022.800.0000001.200.72001.800013.0NaN3.01.020120622.0HEINZHeinzHEINZMARQUEHEINZNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
331.300000e+10Organic Tomato KetchupOrganic Tomato KetchupHeinz['heinz']['heinz']NaNNaN['en:france']NaNNaNNaN['en:france']Epicerie, Sauces, Sauces tomate, Ketchup, Tomato Ketchup{'ciqual_food_name:en': 'Ketchup', 'ciqual_food_name:fr': 'Ketchup'}Fat and saucesDressings and saucesfr:tomato-ketchup20150316.jqm2NaNNaN[][]0.0000000.0[]0.00.0NaN{'fat_unit': '', 'salt_value': 1.201, 'energy-kcal_unit': 'kcal', 'sugars': 23.3, 'carbohydrates_value': 27.2, 'fat_value': 0, 'proteins_value': 1.1, 'energy-kcal_100g': 120, 'proteins': 1.1, 'energy_value': 120, 'proteins_unit': '', 'carbohydrates_100g': 27.2, 'energy_unit': 'kcal', 'sodium_value': 0.48040000000000005, 'salt_100g': 1.201, 'fat': 0, 'proteins_100g': 1.1, 'carbohydrates': 27.2, 'saturated-fat_unit': '', 'saturated-fat_value': 0, 'nutrition-score-fr_100g': 11, 'fat_100g': 0, 'sodium_unit': 'g', 'carbohydrates_unit': '', 'saturated-fat_100g': 0, 'saturated-fat': 0, 'nutrition-score-fr_serving': 11, 'energy': 502, 'sugars_value': 23.3, 'energy-kcal': 120, 'energy_100g': 502, 'salt': 1.201, 'sodium': 0.48040000000000005, 'energy-kcal_value': 120, 'sodium_100g': 0.48040000000000005, 'nutrition-score-fr': 11, 'sugars_100g': 23.3, 'salt_unit': '', 'sugars_unit': ''}502.0120.00.000.027.2023.30NaN1.100.48041.201011.0NaNNaN1.020150316.0HEINZHeinzHEINZMARQUEHEINZNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
441.300053e+10Heinz 57 SauceSauceHeinz['heinz']['heinz']1 Tbsp (17 g)17.0['en:france', 'en:united-states']NaNNaNNaN['en:france', 'en:united-states']Groceries, Sauces{}Fat and saucesDressings and saucesen:sauces20150316.jqm222.0tomato puree (water, tomato paste), high fructose corn syrup, distilled white vinegar, malt vinegar, salt, contains less than 2% of the following: modified food starch, raisin juice concentrate, mustard flour, soybean oil, turmeric, spices, apple puree, sodium benzoate and potassium sorbate (preservatives), caramel color, garlic powder, onion powder, natural flavors,['tomato puree', 'high fructose corn syrup', 'distilled white vinegar', 'malt vinegar', 'salt', 'contains less than 2% of the following', 'raisin', 'mustard flour', 'soybean oil', 'turmeric', 'spices', 'apple puree', 'sodium benzoate', 'potassium sorbate', 'caramel color', 'garlic powder', 'onion powder', 'natural flavors', 'water', 'tomato paste', 'modified food starch', 'preservatives'][{'vegan': 'yes', 'percent_max': 20, 'percent_min': 0, 'vegetarian': 'yes', 'text': 'salt', 'rank': 5, 'id': 'en:salt'}]20.0000000.0[]0.00.0NaN{'fiber_value': 0, 'proteins_serving': 0, 'carbohydrates': 23.53, 'trans-fat_value': 0, 'vitamin-a': 0, 'salt_value': 2352.5, 'trans-fat': 0, 'iron_value': 0, 'salt': 2.3525, 'carbohydrates_100g': 23.53, 'fiber_100g': 0, 'energy-kcal_value': 118, 'nutrition-score-fr_serving': 14, 'fiber': 0, 'iron_serving': 0, 'vitamin-a_unit': 'IU', 'iron_100g': 0, 'vitamin-c_unit': 'mg', 'salt_unit': 'mg', 'energy_serving': 84, 'energy': 494, 'energy_100g': 494, 'fiber_serving': 0, 'proteins': 0, 'proteins_value': 0, 'proteins_unit': 'g', 'sugars_value': 17.65, 'fiber_unit': 'g', 'sodium_100g': 0.941, 'fat_value': 0, 'vitamin-c_100g': 0, 'energy-kcal_100g': 118, 'calcium_unit': 'mg', 'saturated-fat': 0, 'carbohydrates_value': 23.53, 'salt_100g': 2.3525, 'energy_unit': 'kcal', 'cholesterol_value': 0, 'saturated-fat_100g': 0, 'salt_serving': 0.4, 'saturated-fat_unit': 'g', 'trans-fat_unit': 'g', 'cholesterol_100g': 0, 'calcium_value': 0, 'sugars_serving': 3, 'saturated-fat_value': 0, 'iron_unit': 'mg', 'cholesterol': 0, 'nova-group': 4, 'energy_value': 118, 'fat': 0, 'calcium': 0, 'sodium_value': 941, 'saturated-fat_serving': 0, 'energy-kcal_serving': 20.1, 'trans-fat_serving': 0, 'vitamin-a_serving': 0, 'nutrition-score-fr': 14, 'sodium_serving': 0.16, 'calcium_serving': 0, 'sodium_unit': 'mg', 'trans-fat_100g': 0, 'iron': 0, 'vitamin-c': 0, 'proteins_100g': 0, 'carbohydrates_serving': 4, 'nova-group_100g': 4, 'fat_100g': 0, 'nutrition-score-fr_100g': 14, 'energy-kcal': 118, 'sodium': 0.941, 'energy-kcal_unit': 'kcal', 'nova-group_serving': 4, 'fat_serving': 0, 'vitamin-c_value': 0, 'vitamin-a_value': 0, 'carbohydrates_unit': 'g', 'vitamin-a_100g': 0, 'cholesterol_serving': 0, 'sugars_unit': 'g', 'vitamin-c_serving': 0, 'calcium_100g': 0, 'sugars_100g': 17.65, 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 5.555555555555555, 'cholesterol_unit': 'mg', 'fat_unit': 'g', 'sugars': 17.65}494.0118.00.000.023.5317.655.5555560.000.94102.352514.00.04.01.020150316.0HEINZHeinzHEINZMARQUEHEINZNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
551.300053e+10Sauce CaesrSauce CaesrHeinz['heinz']['heinz']NaNNaN['en:france']NaNNaNNaN['en:france']Epicerie, Sauces{}Fat and saucesDressings and saucesen:sauces20150316.jqm2NaNNaN[][]0.0000000.0[]0.00.0NaN{'fat': 18, 'energy-kcal': 170, 'carbohydrates': 1, 'energy_value': 170, 'fat_100g': 18, 'energy-kcal_100g': 170, 'saturated-fat': 3, 'energy-kcal_unit': 'kcal', 'proteins_100g': 0, 'sugars_value': 0, 'fat_value': 18, 'carbohydrates_unit': '', 'energy_unit': 'kcal', 'carbohydrates_value': 1, 'energy-kcal_value': 170, 'carbohydrates_100g': 1, 'saturated-fat_unit': '', 'saturated-fat_100g': 3, 'sugars_unit': '', 'saturated-fat_value': 3, 'proteins': 0, 'energy_100g': 711, 'sugars': 0, 'proteins_value': 0, 'proteins_unit': '', 'fat_unit': '', 'energy': 711, 'sugars_100g': 0}711.0170.03.0018.01.000.00NaN0.00NaNNaNNaNNaNNaN1.020150316.0HEINZHeinzHEINZMARQUEHEINZNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
661.522941e+10NaNSqueeze a little chili pestoSacla['sacla']['sacla']0.25 cup (60 g)60.0['en:france', 'en:united-states']NaNNaNNaN['en:france', 'en:united-states']Groceries, Sauces{}Fat and saucesDressings and saucesen:sauces20150316.jqm222.0Tomato puree, peppers, sunflower seed oil, parsley, almonds, sugar, salt, pecorino romano pdo cheese [ewe's milk, salt, rennet), garlic, modified corn starch, vegetable fiber, dried chili pepper pieces 0.5% sliced chili pepper 0.3%, lactic acid, ground black pepper, preservatives: potassium sorbate, sodium benzoate, thyme powder, aniseed powder.['Tomato puree', 'peppers', 'sunflower seed oil', 'parsley', 'almonds', 'sugar', 'salt', 'pecorino romano pdo cheese', "ewe's milk", 'salt', 'rennet', 'garlic', 'modified corn starch', 'vegetable fiber', 'dried chili pepper pieces 0.5% sliced chili pepper', 'lactic acid', 'black pepper', 'preservatives', 'sodium benzoate', 'thyme', 'aniseed', 'potassium sorbate'][{'id': 'en:salt', 'vegetarian': 'yes', 'rank': 7, 'text': 'salt', 'vegan': 'yes', 'percent_max': 13.942857142857141, 'percent_min': '0.3'}, {'id': 'en:salt', 'vegan': 'yes', 'percent_max': 9.85, 'percent_min': '0.3', 'vegetarian': 'yes', 'rank': 10, 'text': 'salt'}]13.9428570.3[]0.00.0NaN{'fat_100g': 10, 'energy-kcal': 133, 'nutrition-score-fr_100g': 6, 'sodium': 0.517, 'energy-kcal_unit': 'kcal', 'proteins_100g': 1.67, 'nova-group_100g': 3, 'carbohydrates_serving': 5, 'vitamin-a_value': 833, 'carbohydrates_unit': 'g', 'nova-group_serving': 3, 'fat_serving': 6, 'vitamin-c_value': 0, 'vitamin-c_serving': 0, 'vitamin-a_100g': 0.0002499, 'cholesterol_serving': 0, 'sugars_unit': 'g', 'cholesterol_unit': 'mg', 'fat_unit': 'g', 'sugars': 5, 'sugars_100g': 5, 'calcium_100g': 0.033, 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 5.3619047619047615, 'energy_value': 133, 'fat': 10, 'sodium_value': 517, 'calcium': 0.033, 'nova-group': 3, 'cholesterol': 0, 'saturated-fat_serving': 1, 'energy-kcal_serving': 79.8, 'sodium_serving': 0.31, 'calcium_serving': 0.019799999999999998, 'sodium_unit': 'mg', 'trans-fat_serving': 0, 'nutrition-score-fr': 6, 'vitamin-a_serving': 0.00015000000000000001, 'vitamin-c': 0, 'trans-fat_100g': 0, 'iron': 0.0006, 'energy-kcal_100g': 133, 'saturated-fat': 1.67, 'calcium_unit': 'mg', 'sugars_value': 5, 'fiber_unit': 'g', 'sodium_100g': 0.517, 'fat_value': 10, 'vitamin-c_100g': 0, 'salt_100g': 1.2925, 'energy_unit': 'kcal', 'carbohydrates_value': 8.33, 'trans-fat_unit': 'g', 'cholesterol_100g': 0, 'saturated-fat_100g': 1.67, 'salt_serving': 0.7749999999999999, 'cholesterol_value': 0, 'saturated-fat_unit': 'g', 'sugars_serving': 3, 'saturated-fat_value': 1.67, 'iron_unit': 'mg', 'calcium_value': 33, 'proteins_serving': 1, 'carbohydrates': 8.33, 'trans-fat_value': 0, 'vitamin-a': 0.0002499, 'fiber_value': 1.7, 'nutrition-score-fr_serving': 6, 'salt_value': 1292.5, 'trans-fat': 0, 'iron_value': 0.6, 'salt': 1.2925, 'carbohydrates_100g': 8.33, 'fiber_100g': 1.7, 'energy-kcal_value': 133, 'vitamin-c_unit': 'mg', 'iron_100g': 0.0006, 'iron_serving': 0.00035999999999999997, 'fiber': 1.7, 'vitamin-a_unit': 'IU', 'energy_100g': 556, 'fiber_serving': 1.02, 'proteins': 1.67, 'proteins_value': 1.67, 'proteins_unit': 'g', 'salt_unit': 'mg', 'energy': 556, 'energy_serving': 334}556.0133.01.6710.08.335.005.3619051.670.51701.29256.01.73.01.020150316.0SACLASaclaSACLAMARQUESACLANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
773.225067e+104 fromages a la creme et au mascarpone4 fromages a la creme et au mascarponePanzani['panzani']['panzani']NaNNaN['en:france'][]NaNNaN['en:france']Epicerie, Sauces{}Fat and saucesDressings and saucesen:sauces20150316.jqm229.0Eau, fromages 7% (_mascarpone 2,3% - _fromages_ à pâte pressée 2% (_OEUF_) - _emmental_ 1,7% - _roquefort_ 1%), _crème_ 6%, amidons transformés, huile de tournesol, _arômes_, sel, émulsifiants E472e et E471, épaississants : E415 et E414, sels de fonte : E452 et E339, correcteur d'acidité : E500, noix de muscade, poivre, protéines de _lait_, _beurre_, colorant : bêta-carot-ne.['Eau', 'fromages', '_crème_', 'amidons transformés', 'huile de tournesol', '_arômes_', 'sel', 'émulsifiants', 'e471', 'épaississants', 'e414', 'sels de fonte', 'e339', "correcteur d'acidité", 'noix de muscade', 'poivre', 'protéines de _lait_', '_beurre_', 'colorant', '_mascarpone', '_fromages_ à pâte pressée', '_emmental_', '_roquefort_', 'e472e', 'e415', 'e452', 'e500', 'bêta-carot-ne', '_OEUF_'][{'text': 'sel', 'id': 'en:salt', 'percent_min': 0, 'rank': 7, 'vegan': 'yes', 'percent_max': '6', 'vegetarian': 'yes'}]6.0000000.0[{'id': 'en:sunflower-oil', 'text': 'huile de tournesol', 'vegetarian': 'yes', 'vegan': 'yes', 'percent_max': '6', 'from_palm_oil': 'no', 'percent_min': 0, 'rank': 5}]6.00.0huile de tournesol{'carbohydrates_unit': 'g', 'saturated-fat_100g': 3.4, 'sodium_unit': 'g', 'nutrition-score-fr_serving': 8, 'saturated-fat': 3.4, 'carbohydrates': 5.7, 'proteins_100g': 1.9, 'nutrition-score-fr_100g': 8, 'fat_100g': 8.1, 'saturated-fat_unit': 'g', 'saturated-fat_value': 3.4, 'proteins_unit': 'g', 'carbohydrates_100g': 5.7, 'proteins': 1.9, 'energy_value': 104, 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'fat': 8.1, 'energy_unit': 'kcal', 'sodium_value': 0.48, 'salt_100g': 1.2, 'sugars': 0.4, 'energy-kcal_unit': 'kcal', 'carbohydrates_value': 5.7, 'proteins_value': 1.9, 'fat_value': 8.1, 'fat_unit': 'g', 'nova-group_serving': 4, 'salt_value': 1.2, 'energy-kcal_100g': 104, 'sugars_unit': 'g', 'nova-group': 4, 'nova-group_100g': 4, 'salt_unit': 'g', 'sugars_100g': 0.4, 'energy-kcal_value': 104, 'nutrition-score-fr': 8, 'sodium_100g': 0.48, 'carbon-footprint-from-known-ingredients_100g': 34.72, 'energy_100g': 435, 'energy-kcal': 104, 'sugars_value': 0.4, 'energy': 435, 'sodium': 0.48, 'salt': 1.2}435.0104.03.408.15.700.400.0000001.900.48001.20008.0NaN4.01.020150316.0PANZANIPanzaniPANZANIMARQUEPANZANINaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
883.332140e+05TaramaTaramaCasino['casino']['casino']NaNNaN['en:france'][]NaNNaN['en:france']Produits de la mer, Produits à tartiner, Produits à tartiner salés, Taramas{'ciqual_food_name:fr': 'Tarama, préemballé', 'ciqual_food_name:en': 'Taramasalata, prepacked'}Salty snacksSalty and fatty productsen:taramasalata20150316.jqm228.0huile de colza 55% œufs de cabillaud fumés et salés 26% (œufs de cabillaud 25% - sel conser - vateur : E 211 eau - chapelure (farine de blé - eau - sel - levure) - crème (crème de lait - gélifiant : E407) - sel. colorant : E 120 - conservateurs : E262, E202 - acidifiant : E270 - sirop glu. cose déshydraté extraits d'épice, Traces de soja, crustacés, Œufs, cé leri, moutarde, graines de sésame, mollusques et lupin Les informations en gras sont destinées aux personnes intolérantes ou allergiques.['huile de colza 55% œufs de cabillaud fumés et salés', 'œufs de cabillaud', 'sel conser', 'vateur', 'chapelure', 'crème', 'sel', 'colorant', 'conservateurs', 'e202', 'acidifiant', 'sirop glu', "cose déshydraté extraits d'épice", 'cé leri', 'moutarde', 'graines de sésame', 'mollusques et lupin Les informations en gras sont destinées aux personnes intolérantes et allergiques', 'e211 eau', 'farine de blé', 'eau', 'sel', 'levure', 'crème de lait', 'gélifiant', 'e120', 'e262', 'e270', 'e407'][{'rank': 7, 'id': 'en:salt', 'percent_max': 9.457142857142857, 'text': 'sel', 'vegan': 'yes', 'percent_min': 0, 'vegetarian': 'yes'}, {'vegetarian': 'yes', 'vegan': 'yes', 'text': 'sel', 'percent_max': 5.253968253968254, 'percent_min': 0, 'id': 'en:salt'}]9.4571430.0[]0.00.0NaN{'proteins_unit': 'g', 'sugars_unit': 'g', 'sodium': 0.6799999999999999, 'sugars_value': 0.8, 'fiber_100g': 0, 'nova-group_serving': 4, 'proteins_value': 5.6, 'energy-kcal_100g': 548, 'energy-kcal_unit': 'kcal', 'nutrition-score-fr_100g': 16, 'fat_100g': 56, 'carbohydrates_unit': 'g', 'salt_100g': 1.7, 'sugars': 0.8, 'salt': 1.7, 'sodium_value': 0.6799999999999999, 'saturated-fat': 4, 'fiber': 0, 'energy_value': 548, 'nutrition-score-fr_serving': 16, 'sodium_unit': 'g', 'saturated-fat_unit': 'g', 'energy': 2293, 'energy-kcal_value': 548, 'nutrition-score-fr': 16, 'salt_unit': 'g', 'energy-kcal': 548, 'energy_unit': 'kcal', 'carbohydrates_100g': 5.8, 'fat_value': 56, 'fat_unit': 'g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'energy_100g': 2293, 'saturated-fat_value': 4, 'carbohydrates_value': 5.8, 'proteins': 5.6, 'carbohydrates': 5.8, 'fat': 56, 'nova-group_100g': 4, 'sodium_100g': 0.6799999999999999, 'salt_value': 1.7, 'sugars_100g': 0.8, 'saturated-fat_100g': 4, 'nova-group': 4, 'fiber_value': 0, 'proteins_100g': 5.6, 'fiber_unit': 'g'}2293.0548.04.0056.05.800.800.0000005.600.68001.700016.00.04.01.020150316.0CASINONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
994.139001e+10NaNWasabi sauce for sandwiches and dippingKikkoman['kikkoman']['kikkoman']1 tsp (5 g)5.0['en:france', 'en:united-states']NaNNaNNaN['en:france', 'en:united-states']Groceries, Sauces{}Fat and saucesDressings and saucesen:sauces20150316.jqm220.0Water, soybean oil, root blend (horseradish and wasabi), distilled vinegar, high fructose corn syrup, corn starch, sugar, salt, egg yolks, mustard flour, lemon juice concentrate, artificial flavoring, xanthan gum, edta-calcium disodium (to protect flavor and color), natural wasabi flavor, yellow 5, blue 1.['Water', 'soybean oil', 'root blend', 'distilled vinegar', 'high fructose corn syrup', 'corn starch', 'sugar', 'salt', 'egg yolks', 'mustard flour', 'lemon juice concentrate', 'artificial flavoring', 'xanthan gum', 'edta-calcium disodium', 'natural wasabi flavor', 'yellow 5', 'blue 1', 'horseradish', 'wasabi', 'to protect flavor and color'][{'vegetarian': 'yes', 'percent_min': 0, 'text': 'salt', 'percent_max': 12.5, 'vegan': 'yes', 'id': 'en:salt', 'rank': 8}]12.5000000.0[]0.00.0NaN{'saturated-fat_unit': '', 'nutrition-score-fr': 9, 'energy-kcal_value': 200, 'trans-fat': 0, 'trans-fat_unit': 'g', 'energy': 837, 'salt_unit': 'mg', 'energy-kcal': 200, 'salt': 1.75, 'energy-kcal_serving': 10, 'sodium_value': 700, 'energy_value': 200, 'trans-fat_value': 0, 'saturated-fat': 0, 'nutrition-score-fr_serving': 9, 'sodium_unit': 'mg', 'carbohydrates_serving': 1, 'energy_serving': 41.8, 'sugars_serving': 0, 'nutrition-score-fr_100g': 9, 'fat_100g': 20, 'carbohydrates_unit': 'g', 'saturated-fat_serving': 0, 'salt_100g': 1.75, 'sugars': 0, 'sodium_serving': 0.034999999999999996, 'trans-fat_serving': 0, 'sugars_unit': '', 'sugars_value': 0, 'proteins_unit': 'g', 'sodium': 0.7, 'nova-group_serving': 4, 'fat_serving': 1, 'energy-kcal_100g': 200, 'proteins_value': 0, 'energy-kcal_unit': 'kcal', 'trans-fat_100g': 0, 'proteins_serving': 0, 'sodium_100g': 0.7, 'sugars_100g': 0, 'salt_value': 1750, 'nova-group': 4, 'saturated-fat_100g': 0, 'proteins_100g': 0, 'proteins': 0, 'salt_serving': 0.0875, 'fat': 20, 'carbohydrates': 20, 'nova-group_100g': 4, 'energy_100g': 837, 'fat_unit': 'g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g': 0, 'saturated-fat_value': 0, 'carbohydrates_value': 20, 'energy_unit': 'kcal', 'carbohydrates_100g': 20, 'fat_value': 20}837.0200.00.0020.020.000.000.0000000.000.70001.75009.0NaN4.01.020150316.0KIKKOMANKikkomanAUT_MQAUT_MQKIKKOMANNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

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